Martin Prodel Benoit Audry, Carine Jasseron<\/p>
Rank Order of Candidates for Heart Transplantation in France: An Explainable Machine Learning Analysis<\/a> Article de journal<\/span> <\/p>Dans: <\/span>American Journal of Transplantation, <\/span>vol. x, <\/span>2026<\/span>, ISSN: 1600-6135<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>machine learning<\/a><\/p>@article{audry2026rank,\r\ntitle = {Rank Order of Candidates for Heart Transplantation in France: An Explainable Machine Learning Analysis},\r\nauthor = {Benoit Audry, Martin Prodel, Carine Jasseron, Christian Jacquelinet, Naissa Bacar, Pascal Leprince, Laurent Sebbag, Andre Vincentelli, Dahbia Horchi, Benoit Averland, William F. Parker, Michel Tsimaratos, Camille Legeai, Richard Dorent},\r\nurl = {https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012},\r\nissn = {1600-6135},\r\nyear = {2026},\r\ndate = {2026-09-11},\r\nurldate = {2026-09-11},\r\njournal = {American Journal of Transplantation},\r\nvolume = {x},\r\nabstract = {The French composite cardiac allocation score is based on an algorithm that includes interacting components. This multi-step system may hinder understanding of the decision-making process leading to candidate ranking. This study aimed to provide insights into the variables contributing to the ranking of the French transplant candidates. This national cohort study included all candidates ranked between January 2018 and December 2023. The primary endpoint was the normalized ranking of candidates. After determining the most effective machine learning model for predicting ranking, we applied SHapley Additive exPlanations analysis to highlight the relationship between contributing factors and predicted ranking. During the study period, 365,310 rankings were generated, of which 197,684 rankings from 2,430 donors and 3,221 candidates, were other than zero. The post-transplant survival filter was activated in 7% of cases. The top five factors contributing to the ranking were the combined veno-arterial extracorporeal membrane oxygenation\/natriuretic peptide variable, total bilirubin level, transport time from procurement to transplant center, candidate age and estimated glomerular filtration rate. The candidate's sex, blood type, and indication for transplantation had minimal impact on the ranking. These results show that our allocation system combines urgency and efficiency, while limiting influence of variables associated with reduced access to transplant.},\r\nkeywords = {machine learning},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>The French composite cardiac allocation score is based on an algorithm that includes interacting components. This multi-step system may hinder understanding of the decision-making process leading to candidate ranking. This study aimed to provide insights into the variables contributing to the ranking of the French transplant candidates. This national cohort study included all candidates ranked between January 2018 and December 2023. The primary endpoint was the normalized ranking of candidates. After determining the most effective machine learning model for predicting ranking, we applied SHapley Additive exPlanations analysis to highlight the relationship between contributing factors and predicted ranking. During the study period, 365,310 rankings were generated, of which 197,684 rankings from 2,430 donors and 3,221 candidates, were other than zero. The post-transplant survival filter was activated in 7% of cases. The top five factors contributing to the ranking were the combined veno-arterial extracorporeal membrane oxygenation\/natriuretic peptide variable, total bilirubin level, transport time from procurement to transplant center, candidate age and estimated glomerular filtration rate. The candidate's sex, blood type, and indication for transplantation had minimal impact on the ranking. These results show that our allocation system combines urgency and efficiency, while limiting influence of variables associated with reduced access to transplant.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>2.<\/div>Jan Kehrmann Olivier Dauwalder, St\u00e9phane Corvec<\/p>Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Microorganisms, <\/span>vol. 14, <\/span>iss. 9, <\/span>no. 2011, <\/span>2026<\/span>, ISSN: 2076-2607<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Dans: <\/span>American Journal of Transplantation, <\/span>vol. x, <\/span>2026<\/span>, ISSN: 1600-6135<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>machine learning<\/a><\/p>@article{audry2026rank,\r\ntitle = {Rank Order of Candidates for Heart Transplantation in France: An Explainable Machine Learning Analysis},\r\nauthor = {Benoit Audry, Martin Prodel, Carine Jasseron, Christian Jacquelinet, Naissa Bacar, Pascal Leprince, Laurent Sebbag, Andre Vincentelli, Dahbia Horchi, Benoit Averland, William F. Parker, Michel Tsimaratos, Camille Legeai, Richard Dorent},\r\nurl = {https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012},\r\nissn = {1600-6135},\r\nyear = {2026},\r\ndate = {2026-09-11},\r\nurldate = {2026-09-11},\r\njournal = {American Journal of Transplantation},\r\nvolume = {x},\r\nabstract = {The French composite cardiac allocation score is based on an algorithm that includes interacting components. This multi-step system may hinder understanding of the decision-making process leading to candidate ranking. This study aimed to provide insights into the variables contributing to the ranking of the French transplant candidates. This national cohort study included all candidates ranked between January 2018 and December 2023. The primary endpoint was the normalized ranking of candidates. After determining the most effective machine learning model for predicting ranking, we applied SHapley Additive exPlanations analysis to highlight the relationship between contributing factors and predicted ranking. During the study period, 365,310 rankings were generated, of which 197,684 rankings from 2,430 donors and 3,221 candidates, were other than zero. The post-transplant survival filter was activated in 7% of cases. The top five factors contributing to the ranking were the combined veno-arterial extracorporeal membrane oxygenation\/natriuretic peptide variable, total bilirubin level, transport time from procurement to transplant center, candidate age and estimated glomerular filtration rate. The candidate's sex, blood type, and indication for transplantation had minimal impact on the ranking. These results show that our allocation system combines urgency and efficiency, while limiting influence of variables associated with reduced access to transplant.},\r\nkeywords = {machine learning},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>The French composite cardiac allocation score is based on an algorithm that includes interacting components. This multi-step system may hinder understanding of the decision-making process leading to candidate ranking. This study aimed to provide insights into the variables contributing to the ranking of the French transplant candidates. This national cohort study included all candidates ranked between January 2018 and December 2023. The primary endpoint was the normalized ranking of candidates. After determining the most effective machine learning model for predicting ranking, we applied SHapley Additive exPlanations analysis to highlight the relationship between contributing factors and predicted ranking. During the study period, 365,310 rankings were generated, of which 197,684 rankings from 2,430 donors and 3,221 candidates, were other than zero. The post-transplant survival filter was activated in 7% of cases. The top five factors contributing to the ranking were the combined veno-arterial extracorporeal membrane oxygenation\/natriuretic peptide variable, total bilirubin level, transport time from procurement to transplant center, candidate age and estimated glomerular filtration rate. The candidate's sex, blood type, and indication for transplantation had minimal impact on the ranking. These results show that our allocation system combines urgency and efficiency, while limiting influence of variables associated with reduced access to transplant.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>2.<\/div>Jan Kehrmann Olivier Dauwalder, St\u00e9phane Corvec<\/p>Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Microorganisms, <\/span>vol. 14, <\/span>iss. 9, <\/span>no. 2011, <\/span>2026<\/span>, ISSN: 2076-2607<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>machine learning<\/a><\/p>@article{audry2026rank,\r\ntitle = {Rank Order of Candidates for Heart Transplantation in France: An Explainable Machine Learning Analysis},\r\nauthor = {Benoit Audry, Martin Prodel, Carine Jasseron, Christian Jacquelinet, Naissa Bacar, Pascal Leprince, Laurent Sebbag, Andre Vincentelli, Dahbia Horchi, Benoit Averland, William F. Parker, Michel Tsimaratos, Camille Legeai, Richard Dorent},\r\nurl = {https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012},\r\nissn = {1600-6135},\r\nyear = {2026},\r\ndate = {2026-09-11},\r\nurldate = {2026-09-11},\r\njournal = {American Journal of Transplantation},\r\nvolume = {x},\r\nabstract = {The French composite cardiac allocation score is based on an algorithm that includes interacting components. This multi-step system may hinder understanding of the decision-making process leading to candidate ranking. This study aimed to provide insights into the variables contributing to the ranking of the French transplant candidates. This national cohort study included all candidates ranked between January 2018 and December 2023. The primary endpoint was the normalized ranking of candidates. After determining the most effective machine learning model for predicting ranking, we applied SHapley Additive exPlanations analysis to highlight the relationship between contributing factors and predicted ranking. During the study period, 365,310 rankings were generated, of which 197,684 rankings from 2,430 donors and 3,221 candidates, were other than zero. The post-transplant survival filter was activated in 7% of cases. The top five factors contributing to the ranking were the combined veno-arterial extracorporeal membrane oxygenation\/natriuretic peptide variable, total bilirubin level, transport time from procurement to transplant center, candidate age and estimated glomerular filtration rate. The candidate's sex, blood type, and indication for transplantation had minimal impact on the ranking. These results show that our allocation system combines urgency and efficiency, while limiting influence of variables associated with reduced access to transplant.},\r\nkeywords = {machine learning},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>The French composite cardiac allocation score is based on an algorithm that includes interacting components. This multi-step system may hinder understanding of the decision-making process leading to candidate ranking. This study aimed to provide insights into the variables contributing to the ranking of the French transplant candidates. This national cohort study included all candidates ranked between January 2018 and December 2023. The primary endpoint was the normalized ranking of candidates. After determining the most effective machine learning model for predicting ranking, we applied SHapley Additive exPlanations analysis to highlight the relationship between contributing factors and predicted ranking. During the study period, 365,310 rankings were generated, of which 197,684 rankings from 2,430 donors and 3,221 candidates, were other than zero. The post-transplant survival filter was activated in 7% of cases. The top five factors contributing to the ranking were the combined veno-arterial extracorporeal membrane oxygenation\/natriuretic peptide variable, total bilirubin level, transport time from procurement to transplant center, candidate age and estimated glomerular filtration rate. The candidate's sex, blood type, and indication for transplantation had minimal impact on the ranking. These results show that our allocation system combines urgency and efficiency, while limiting influence of variables associated with reduced access to transplant.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>2.<\/div>Jan Kehrmann Olivier Dauwalder, St\u00e9phane Corvec<\/p>Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Microorganisms, <\/span>vol. 14, <\/span>iss. 9, <\/span>no. 2011, <\/span>2026<\/span>, ISSN: 2076-2607<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
@article{audry2026rank,\r\ntitle = {Rank Order of Candidates for Heart Transplantation in France: An Explainable Machine Learning Analysis},\r\nauthor = {Benoit Audry, Martin Prodel, Carine Jasseron, Christian Jacquelinet, Naissa Bacar, Pascal Leprince, Laurent Sebbag, Andre Vincentelli, Dahbia Horchi, Benoit Averland, William F. Parker, Michel Tsimaratos, Camille Legeai, Richard Dorent},\r\nurl = {https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012},\r\nissn = {1600-6135},\r\nyear = {2026},\r\ndate = {2026-09-11},\r\nurldate = {2026-09-11},\r\njournal = {American Journal of Transplantation},\r\nvolume = {x},\r\nabstract = {The French composite cardiac allocation score is based on an algorithm that includes interacting components. This multi-step system may hinder understanding of the decision-making process leading to candidate ranking. This study aimed to provide insights into the variables contributing to the ranking of the French transplant candidates. This national cohort study included all candidates ranked between January 2018 and December 2023. The primary endpoint was the normalized ranking of candidates. After determining the most effective machine learning model for predicting ranking, we applied SHapley Additive exPlanations analysis to highlight the relationship between contributing factors and predicted ranking. During the study period, 365,310 rankings were generated, of which 197,684 rankings from 2,430 donors and 3,221 candidates, were other than zero. The post-transplant survival filter was activated in 7% of cases. The top five factors contributing to the ranking were the combined veno-arterial extracorporeal membrane oxygenation\/natriuretic peptide variable, total bilirubin level, transport time from procurement to transplant center, candidate age and estimated glomerular filtration rate. The candidate's sex, blood type, and indication for transplantation had minimal impact on the ranking. These results show that our allocation system combines urgency and efficiency, while limiting influence of variables associated with reduced access to transplant.},\r\nkeywords = {machine learning},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>The French composite cardiac allocation score is based on an algorithm that includes interacting components. This multi-step system may hinder understanding of the decision-making process leading to candidate ranking. This study aimed to provide insights into the variables contributing to the ranking of the French transplant candidates. This national cohort study included all candidates ranked between January 2018 and December 2023. The primary endpoint was the normalized ranking of candidates. After determining the most effective machine learning model for predicting ranking, we applied SHapley Additive exPlanations analysis to highlight the relationship between contributing factors and predicted ranking. During the study period, 365,310 rankings were generated, of which 197,684 rankings from 2,430 donors and 3,221 candidates, were other than zero. The post-transplant survival filter was activated in 7% of cases. The top five factors contributing to the ranking were the combined veno-arterial extracorporeal membrane oxygenation\/natriuretic peptide variable, total bilirubin level, transport time from procurement to transplant center, candidate age and estimated glomerular filtration rate. The candidate's sex, blood type, and indication for transplantation had minimal impact on the ranking. These results show that our allocation system combines urgency and efficiency, while limiting influence of variables associated with reduced access to transplant.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>2.<\/div>Jan Kehrmann Olivier Dauwalder, St\u00e9phane Corvec<\/p>Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Microorganisms, <\/span>vol. 14, <\/span>iss. 9, <\/span>no. 2011, <\/span>2026<\/span>, ISSN: 2076-2607<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div>The French composite cardiac allocation score is based on an algorithm that includes interacting components. This multi-step system may hinder understanding of the decision-making process leading to candidate ranking. This study aimed to provide insights into the variables contributing to the ranking of the French transplant candidates. This national cohort study included all candidates ranked between January 2018 and December 2023. The primary endpoint was the normalized ranking of candidates. After determining the most effective machine learning model for predicting ranking, we applied SHapley Additive exPlanations analysis to highlight the relationship between contributing factors and predicted ranking. During the study period, 365,310 rankings were generated, of which 197,684 rankings from 2,430 donors and 3,221 candidates, were other than zero. The post-transplant survival filter was activated in 7% of cases. The top five factors contributing to the ranking were the combined veno-arterial extracorporeal membrane oxygenation\/natriuretic peptide variable, total bilirubin level, transport time from procurement to transplant center, candidate age and estimated glomerular filtration rate. The candidate's sex, blood type, and indication for transplantation had minimal impact on the ranking. These results show that our allocation system combines urgency and efficiency, while limiting influence of variables associated with reduced access to transplant.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>2.<\/div>Jan Kehrmann Olivier Dauwalder, St\u00e9phane Corvec<\/p>Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Microorganisms, <\/span>vol. 14, <\/span>iss. 9, <\/span>no. 2011, <\/span>2026<\/span>, ISSN: 2076-2607<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/i>https:\/\/www.amjtransplant.org\/article\/S1600-6135(26)02755-3\/abstract<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.ajt.2026.09.012<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>2.<\/div>Jan Kehrmann Olivier Dauwalder, St\u00e9phane Corvec<\/p>Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Microorganisms, <\/span>vol. 14, <\/span>iss. 9, <\/span>no. 2011, <\/span>2026<\/span>, ISSN: 2076-2607<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/div><\/div>2.<\/div>Jan Kehrmann Olivier Dauwalder, St\u00e9phane Corvec<\/p>Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Microorganisms, <\/span>vol. 14, <\/span>iss. 9, <\/span>no. 2011, <\/span>2026<\/span>, ISSN: 2076-2607<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Jan Kehrmann Olivier Dauwalder, St\u00e9phane Corvec<\/p>
Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Microorganisms, <\/span>vol. 14, <\/span>iss. 9, <\/span>no. 2011, <\/span>2026<\/span>, ISSN: 2076-2607<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Dans: <\/span>Microorganisms, <\/span>vol. 14, <\/span>iss. 9, <\/span>no. 2011, <\/span>2026<\/span>, ISSN: 2076-2607<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a><\/p>@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
@article{nokey,\r\ntitle = {Multicentre Evaluation of Cepheid Xpert\u00ae Research-Use-Only Panel for Molecular Rapid Detection of Gastrointestinal Pathogens},\r\nauthor = {Olivier Dauwalder, Jan Kehrmann, St\u00e9phane Corvec, Maroussia Roelens, Tiphaine Roussel-Gaillard, Edoardo Bixio, Jan Buer, Elisa Baillemont, Martin Prodel, Valeria Cento},\r\nurl = {https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011},\r\ndoi = {10.3390\/microorganisms14092011},\r\nissn = {2076-2607},\r\nyear = {2026},\r\ndate = {2026-09-10},\r\njournal = {Microorganisms},\r\nvolume = {14},\r\nnumber = {2011},\r\nissue = {9},\r\nabstract = {Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.},\r\nkeywords = {biostatistics},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div>Rapid and accurate identification of enteric pathogens is essential for the management of acute infectious diarrhoeal diseases. Multiplex PCR panels have emerged as first-line screening tools to conventional stool culture by providing broad pathogen coverage and shorter turnaround times. This study evaluated the diagnostic performance of the Cepheid Xpert gastrointestinal (GI) research-use-only (RUO) panel in routine clinical practice across multiple European centres. A multicentre study was conducted in four hospitals across France, Germany and Italy. Stool specimens collected as part of routine care were tested using local standard-of-care (SoC) methods, including conventional stool culture, BDMax\u00ae Enteric Panels, or BioFire FilmArray\u00ae GI Panel, and subsequently analysed using the Cepheid Xpert GI RUO panel. A total of 11 bacterial, viral and parasitic enteropathogens were targeted. Discordant results underwent additional testing, and a consensus result based on a two-out-of-three agreement rule was used as the reference standard. Diagnostic performance metrics, agreement with SoC methods, microbiological findings and turnaround times were assessed. Of 508 samples, 115 (22.6%) were identified as positive for at least one pathogen according to the consensus result, including 12 samples with co-detection of more than one pathogen (10.4% of positive samples), resulting in a total of 128 pathogens detected. The most frequently detected pathogens were Campylobacter spp. (37 specimens), Norovirus (26 specimens), and Salmonella spp. (25 specimens). Compared with the consensus reference standard, Xpert achieved an overall sensitivity of 98% (95% CI: 94\u2013100%), specificity of 100% (95% CI: 100\u2013100%), positive predictive value of 95% (95% CI: 90\u201398%), and negative predictive value of 100% (95% CI: 100\u2013100%). Sensitivity was \u226595% and specificity \u226599% for all evaluable targets. Agreement with routine diagnostic methods was high, with overall positive and negative percent agreements exceeding 90% and a Cohen's kappa coefficient of 0.91. Median analytical turnaround time was 1 h 18 min for Xpert, compared with 3 h for BDMax, 1 h 15 min for BioFire and 48 h for conventional stool culture. The Cepheid Xpert GI (RUO) panel provided highly accurate detection of enteric pathogens across heterogeneous European diagnostic settings and reduced turnaround time by more than 95% compared with conventional stool culture. A future IVD version of this test could provide rapid and accurate detection of enteric pathogens for potential routine management of patients with suspected acute infectious gastroenteritis.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/i>https:\/\/www.mdpi.com\/2076-2607\/14\/9\/2011<\/a><\/li><\/i>doi:10.3390\/microorganisms14092011<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/div><\/div>3.<\/div>Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Martin Prodel Ludovic Lamarsalle, Stephane Roze<\/p>
What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?<\/a> Actes<\/span> <\/p>vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
vol. 28, <\/span>no. S539, <\/span>2025<\/span>, ISSN: 1098-3015<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
@proceedings{lamarsalle2025value,\r\ntitle = {What Is the Value of Large Language Models for Improving Extraction and Interpretation of Efficiency Data From French Commission D\u00e9valuation \u00c9conomique et de Sant\u00e9 Publique (CEESP): Decisions on Breast Cancer Medications?},\r\nauthor = {Ludovic Lamarsalle, Martin Prodel, Stephane Roze},\r\nurl = {https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495},\r\ndoi = {10.1016\/j.jval.2025.09.3495},\r\nissn = {1098-3015},\r\nyear = {2025},\r\ndate = {2025-12-01},\r\njournal = {Value in Health},\r\nvolume = {28},\r\nnumber = {S539},\r\nissue = {12, Supplement 1},\r\nabstract = {Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {proceedings}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div>Objectives\r\nData insight of health-economic evidence is pivotal to understand reimbursement drivers. The economic committee (CEESP) of the French Haute Autorit\u00e9 de Sant\u00e9 conducts cost-effectiveness assessments for products that claim added clinical benefit (ASMR I-III) and are expected to exceed \u20ac20 million in annual sales. We assessed whether state-of-the-art Large Language Models (LLMs) can perform data extraction from recent CEESP breast-cancer opinions.\r\n\r\nMethods\r\nSixteen opinions released between March 2014 and April 2024 were analyzed with three LLMs (ChatGPT-4o, Claude-3-5-Sonnet, Mistral-Large 24.11) using a unified prompt covering 65 data fields: 23 administrative and 42 health-economic variables (e.g., incremental cost-effectiveness ratio, modeling assumptions, utilities\u22ef). LLM outputs were compared pair-wise and against a manually curated reference created by senior health-economists.\r\n\r\nResults\r\nThe analysis of 16 CEESP opinions revealed two performance patterns. Inter-LLM similarity analysis showed moderate concordance: perfect agreement among all three LLMs was achieved in 33.8% of fields, with partial agreement (\u22652 LLMs) in 70.2% of cases. Structured administrative fields demonstrated high inter-model consistency: intervention type (100% agreement), therapeutic area (100%), and regulatory parameters (>90% agreement). However, when compared against expert-validated reference data, overall performance was lower. ChatGPT4o achieved the highest similarity score (0.550), followed by Claude-3.5-Sonnet (0.537) and Mistral-Large (0.519). Important discrepancies emerged in medico-economic fields: target population extraction, cost-effectiveness ratio values, and efficiency condition. Only 31% of fields achieved excellent reference concordance (\u226590%), while 52.4% showed moderate performance (<70%). Complex numerical data and free-text clinical descriptions posed the greatest challenges, indicating that LLM consensus does not guarantee accuracy against expert validation.\r\n\r\nConclusions\r\nLLMs demonstrate promising potential for automating health economic data extraction from regulatory documents. However, limitations persist for complex medico-economic parameters requiring specialized domain knowledge. A multi-LLM consensus approach with automated disagreement detection could trigger expert review only for discordant results, significantly reducing manual workload while maintaining quality standards.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/i>https:\/\/doi.org\/10.1016\/j.jval.2025.09.3495<\/a><\/li><\/i>doi:10.1016\/j.jval.2025.09.3495<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/div><\/div>4.<\/div> Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Huguet, Marius; Pehlivan, Canan; Ballereau, Fran\u00e7ois; Dodane-Loyenet, Antoine; Fontanili, Franck; Garaix, Thierry; Yordanov, Youri; Augusto, Vincent; Tazarourte, Karim; Redjaline, Abdesslam<\/p>
Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Dans: <\/span>Communications Medicine, <\/span>vol. 5, <\/span>no. 1, <\/span>2025<\/span>, (Cited by: 0; All Open Access, Gold Open Access)<\/span>.<\/p>Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span><\/p>@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
@article{Huguet2025,\r\ntitle = {Indoor positioning systems provide insight into emergency department systems enabling proposal of designs to improve workflow},\r\nauthor = {Marius Huguet and Canan Pehlivan and Fran\u00e7ois Ballereau and Antoine Dodane-Loyenet and Franck Fontanili and Thierry Garaix and Youri Yordanov and Vincent Augusto and Karim Tazarourte and Abdesslam Redjaline},\r\nurl = {https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038%2fs43856-025-00793-y&partnerID=40&md5=0c54a21314363953d989ec26aab4c7b5},\r\ndoi = {10.1038\/s43856-025-00793-y},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Communications Medicine},\r\nvolume = {5},\r\nnumber = {1},\r\nnote = {Cited by: 0; All Open Access, Gold Open Access},\r\nkeywords = {},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/i>https:\/\/www.scopus.com\/inward\/record.uri?eid=2-s2.0-105000073700&doi=10.1038[...]<\/a><\/li><\/i>doi:10.1038\/s43856-025-00793-y<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/div><\/div>5.<\/div> Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Luu, VP; Denis, H\u00e9l\u00e8ne; Bensimon, L; Durand-Zaleski, I; Greillier, L; Thomas, P; Wislez, M; Panes, A; Prodel, Martin; Apert, M; others,<\/p>
LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining) Article de journal<\/span> <\/p>Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Dans: <\/span>Revue des Maladies Respiratoires Actualit\u00e9s, <\/span>vol. 17, <\/span>no. 1, <\/span>p. 245\u2013246, <\/span>2025<\/span>.<\/p>BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>pathways<\/a>, process mining<\/a><\/p>@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
@article{luu2025lucien,\r\ntitle = {LUCIEN\u2013\u00c9tude non interventionnelle sur les d\u00e9lais et les co\u00fbts des parcours diagnostiques du cancer du poumon primitif: analyse de processus (process mining)},\r\nauthor = {VP Luu and H\u00e9l\u00e8ne Denis and L Bensimon and I Durand-Zaleski and L Greillier and P Thomas and M Wislez and A Panes and Martin Prodel and M Apert and others},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Revue des Maladies Respiratoires Actualit\u00e9s},\r\nvolume = {17},\r\nnumber = {1},\r\npages = {245\u2013246},\r\npublisher = {Elsevier Masson},\r\nkeywords = {pathways, process mining},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/div><\/div>6.<\/div> Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Perrot, Aurore; Raguideau, Fanny; Denis, Helene; Prodel, Martin; Javelot, Matthieu; Pierres, Marie; Touzeau, Cyrille<\/p>
Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France<\/a> Article de journal<\/span> <\/p>Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Dans: <\/span>Clinical Lymphoma Myeloma and Leukemia, <\/span>2025<\/span>, ISSN: 2152-2650<\/span>.<\/p>R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
R\u00e9sum\u00e9<\/a><\/span> | Liens<\/a><\/span> | BibTeX<\/a><\/span> | \u00c9tiquettes: <\/span>biostatistics<\/a>, pathways<\/a><\/p>@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
@article{PERROT2025,\r\ntitle = {Survival, Years of Life Lost and Attrition Rates in Multiple Myeloma Patients in France},\r\nauthor = {Aurore Perrot and Fanny Raguideau and Helene Denis and Martin Prodel and Matthieu Javelot and Marie Pierres and Cyrille Touzeau},\r\nurl = {https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801},\r\ndoi = {https:\/\/doi.org\/10.1016\/j.clml.2025.03.004},\r\nissn = {2152-2650},\r\nyear = {2025},\r\ndate = {2025-01-01},\r\nurldate = {2025-01-01},\r\njournal = {Clinical Lymphoma Myeloma and Leukemia},\r\nabstract = {Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.},\r\nkeywords = {biostatistics, pathways},\r\npubstate = {published},\r\ntppubtype = {article}\r\n}\r\n<\/pre><\/div>Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div>Background \r\nIn a context of strong evolution of multiple myeloma (MM) treatment paradigm, real-world data allow a better understandingthe patients' medical needs. \r\nMethods \r\nThe present analyses from the MYLORD study were designed to provide recent data on MM patients: characteristics, overall survival (OS), years of life lost (YLL) and attrition rates, using the French National Health Insurance Database (SNDS). It is based on a cohort of 33,032 MM patients who initiated a frontline therapy from 2014 to 2021 in France and who were followed until 2021. \r\nResults \r\nThe 5-year OS rate is estimated at 51.9% (95% CI: 51.2\u201352.6). This rate is 78.3% (95% CI: 77.1\u201379.5) for patients with frontline autologous stem cell transplant (L1T) and 43.6% (95% CI: 42.8\u201344.4) for those without transplant (L1NT). The excess of mortality in MM patients is revealed by 12.3 YLL versus the matched general population and is higher in L1T patients than those L1NT (17 vs. 11 years). The attrition rate is important in MM patients with 38% of patients who did not benefit from at least 2 lines of treatment and is higher in L1NT patients than those in L1T (41% vs. 29%). Also, the majority of L1NT patients did not receive a L2 because of death, whereas it is because of stay in line status for L1T patients. \r\nConclusion \r\nThese results emphasize the urgency to treat MM patients with the best and tailored treatment strategy available, for all MM patients beneficiating or not from a transplant in frontline therapy. Further studies will be needed to assess the impact of new therapeutic options in real life.<\/div>Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/i>https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2152265025000801<\/a><\/li><\/i>doi:https:\/\/doi.org\/10.1016\/j.clml.2025.03.004<\/a><\/li><\/ul><\/div>Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Fermer<\/a><\/p><\/div><\/div><\/div>7.<\/div> Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Peng, Zhihao; Augusto, Vincent; Perrier, Lionel; Xie, Xiaolan<\/p>
Optimal Process Mining of Traces with Events and Transition Attributes with Application to Care Pathways of Cancer Patients<\/a> Article de journal<\/span> <\/p>Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>
Dans: <\/span>IEEE Transactions on Automation Science and Engineering, <\/span>vol. 21, <\/span>no. 3, <\/span>p. 4364 \u2013 4381, <\/span>2024<\/span>