[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121691-en":3,"doc-seo-121691-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121691,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Prediction of early death after atrial fibrillation diagnosis using a machine learning approach - A French nationwide cohort study","Atrial fibrillation is linked to substantial mortality, while conventional clinical risk-score approaches show limited predictive performance. This study developed machine learning models to predict death within one year after atrial fibrillation diagnosis and compared them with established clinical risk scores. Using a French nationwide cohort of 2,435,541 patients diagnosed from 2011–2019, the best deep neural network achieved superior discrimination on the validation set and outperformed CHA2DS2-VASc, HASBLED, and several dedicated indices. ","Journal Pre-proof  \nPrediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study  \nArnaud Bisson MD , Yassine Lemrini MD ,  \nGiulio Francesco Romiti MD , Marco Proietti MD PhD , Denis Angoulvant MD PhD , Sidahmed Bentounes , Wahbi El-Bouri PhD , Gregory Y. H. Lip MD ,  \nLaurent Fauchier MD PhD  \nPII: S0002-8703(23)00203-X  \nDOI: [https://doi.org/10.1016/j.ahj.2023.08.006](https://doi.org/10.1016/j.ahj.2023.08.006)  \nReference: YMHJ 6816  \nTo appear in: American Heart Journal  \nReceived date: August 14, 2023  \nAccepted date: August 14, 2023  \nPlease cite this article as: Arnaud Bisson MD , Yassine Lemrini MD , Giulio Francesco Romiti MD , Marco Proietti MD PhD , Denis Angoulvant MD PhD , Sidahmed Bentounes , Wahbi El-Bouri PhD , Gregory Y. H. Lip MD , Laurent Fauchier MD PhD , Prediction of early death after atrial fibrillation diagnosis using a machine learning approach: A French nationwide cohort study, American Heart Journal  \n(2023), doi: [https://doi.org/10.1016/j.ahj.2023.08.006](https://doi.org/10.1016/j.ahj.2023.08.006)  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2023 Published by Elsevier Inc.  \nPrediction of early death after atrial fibrillation diagnosis using a machine learning approach:  \nA French nationwide cohort study  \nArnaud BISSON 1,2,3,4 MD, Yassine LEMRINI 1 MD, Giulio Francesco ROMITI4,5 MD, Marco PROIETTI4,6,7 MD PhD, Denis ANGOULVANT1,2 MD PhD, Sidahmed BENTOUNES 1, Wahbi EL-BOURI4 PhD, Gregory Y. H. LIP4,8* MD, Laurent FAUCHIER 1 MD PhD*  \n[*Joint senior authors]  \n1-Service de Cardiologie, Centre Hospitalier Régional Universitaire et Faculté de Médecine de Tours, Tours, France  \n2-EA4245, Transplantation Immunité Inflammation, Université de Tours, Tours, France  \n3-Service de Cardiologie, Centre Hospitalier Régional Universitaire d’Orléans, Orléans, France  \n4-Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, United Kingdom  \n5-Department of Translational and Precision Medicine, Sapienza – University of Rome, Italy;  \n6-Department of Clinical Sciences and Community Health, University of Milan, Italy;  \n7-Division of Subacute Care, IRCCS Istituti Clinici Scientifici Maugeri, Milano, Italy;  \n8-Danish Center for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark;  \nCorrespondence:  \nArnaud BISSON, MD  \nService de Cardiologie, Centre Hospitalier Regional Universitaire et Faculté de Médecine de Tours, Tours, France  \n2 Boulevard Tonnellé, 37000 Tours  \nTel : 33-247473659  \nEmail: [arnaud.bisson@univ-tours.fr](arnaud.bisson@univ-tours.fr)  \n[Total word count:](Total word count: 4)[ 4](Total word count: 4),293  \nCategory: Original article  \nShort title: Prediction of early death after atrial fibrillation  \nKey words: Atrial fibrillation, Prediction, Machine learning, Mortality.  \nAbstract:  \nAims: Atrial fibrillation is associated with important mortality but the usual clinical risk factor based scores only modestly predict mortality. This study aimed to develop machine learning models for the prediction of death occurrence within the year following atrial fibrillation diagnosis and compare predictive ability against usual clinical risk scores.  \nMethods and Results: We used a nationwide cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in French hospit","cbCaipOZIdZV0Dfl","https://ap.wps.com/l/cbCaipOZIdZV0Dfl","pdf",1840979,1,34,"English","en",105,"# Abstract\n## Aims\n## Methods and Results\n## Conclusion\n## Translational Perspective\n# Introduction","[{\"question\":\"What was the study objective?\",\"answer\":\"To develop machine learning models predicting death within one year after atrial fibrillation diagnosis and to compare their performance with usual clinical risk scores.\"},{\"question\":\"What data were used to train and validate the models?\",\"answer\":\"A nationwide French cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in hospitals from 2011 to 2019, split into a 70% training set and a 30% validation set.\"},{\"question\":\"Which machine learning model performed best and how was it evaluated?\",\"answer\":\"A deep neural network performed best, with discrimination assessed using the C index on the validation set (C index reported as 0.785 with a 95% CI).\"},{\"question\":\"How did the best model compare with existing clinical risk scores?\",\"answer\":\"The selected model was superior to CHA2DS2-VASc and HASBLED and also outperformed dedicated scores including the Charlson Comorbidity Index and the Hospital Frailty Risk Score for predicting death within the year after diagnosis.\"}]","Prediction of early death after atrial fibrillation diagnosis using a machine learning approach - A French nationwide cohort study | PDF",1785806286,86,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":90,"head_meta":92,"extra_data":94,"updated_unix":28},"prediction-of-early-death-after-atrial-fibrillation-diagnosis-using-a-machine-learning-approach-a-french-nationwide-cohort-study","",{"@graph":36,"@context":89},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/prediction-of-early-death-after-atrial-fibrillation-diagnosis-using-a-machine-learning-approach-a-french-nationwide-cohort-study/121691/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What was the study objective?","Question",{"text":75,"@type":76},"To develop machine learning models predicting death within one year after atrial fibrillation diagnosis and to compare their performance with usual clinical risk scores.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data were used to train and validate the models?",{"text":80,"@type":76},"A nationwide French cohort of 2,435,541 newly diagnosed atrial fibrillation patients seen in hospitals from 2011 to 2019, split into a 70% training set and a 30% validation set.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and how was it evaluated?",{"text":84,"@type":76},"A deep neural network performed best, with discrimination assessed using the C index on the validation set (C index reported as 0.785 with a 95% CI).",{"name":86,"@type":73,"acceptedAnswer":87},"How did the best model compare with existing clinical risk scores?",{"text":88,"@type":76},"The selected model was superior to CHA2DS2-VASc and HASBLED and also outperformed dedicated scores including the Charlson Comorbidity Index and the Hospital Frailty Risk Score for predicting death within the year after diagnosis.","https://schema.org",{"og:url":52,"og:type":91,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":93,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":96},[97,101,105,109,114,119,124,127,132,135,139],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Exam",70,"exam",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},5,"Comic",60,"comic",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},6,"Technology",50,"technology",{"id":120,"doc_module":4,"doc_module_name":46,"category_name":121,"show_sort_weight":122,"slug":123},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":125,"slug":126},30,"research-report",{"id":128,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":130,"slug":131},9,"Religion & Spirituality",20,"religion-spirituality",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":130,"slug":134},"World Cup","world-cup",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":136,"slug":138},10,"Lifestyle","lifestyle",{"id":140,"doc_module":4,"doc_module_name":46,"category_name":141,"show_sort_weight":110,"slug":142},19,"General","general"]