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AF arises from complex, dynamic interactions between risk factors and comorbidities that trigger diverse atrial remodelling processes, increasing vulnerability and persistence and promoting disease progression. Variability in mechanisms makes early identification of modifiable causal factors difficult. Computational modelling enables multilevel integration of datasets and supports mechanistic understanding, risk prediction and personalized therapy, with narrative review coverage of emerging applications, available in silico approaches, and limitations.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/computational-models-of-atrial-fibrillation-achievements-challenges-and-perspectives-for-improving-clinical-care/505023/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/computational-models-of-atrial-fibrillation-achievements-challenges-and-perspectives-for-improving-clinical-care/505023.png","ImageObject",300,407,{"name":92,"@type":93},"Connor ","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-10-04",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",13,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is early identification of causal factors in atrial fibrillation challenging?","Question",{"text":113,"@type":114},"AF involves diverse mechanisms for initiation, maintenance and progression, and its clinical presentation varies widely. This complexity makes early detection of modifiable causal factors with therapeutic interventions difficult.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does computational modelling contribute to atrial fibrillation research and clinical care?",{"text":118,"@type":114},"Computational modelling integrates multiple datasets across levels, improving mechanistic understanding. It also supports risk prediction and personalized therapy through mathematical simulations of cardiac electrophysiology.",{"name":120,"@type":111,"acceptedAnswer":121},"What does the narrative review focus on regarding computational models in AF management?",{"text":122,"@type":114},"The review summarizes clinical challenges that could benefit from computational modelling, outlines the main in silico approaches and their achievements, and discusses major limitations that hinder routine clinical use. It concludes with future perspectives.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},505023,1791297615,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},687207022233,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Cardiovascular Research (2021) 117, 1682–1699 doi:10.1093/cvr/cvab138  \nINVITED SPOTLIGHT REVIEW  \nComputational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care  \nJordi Heijman  1*, Henry Sutanto  1, Harry J.G. M. Crijns  1, Stanley Nattel  2,3,4,5, and Natalia A. Trayanova  6,7  \n1Department of Cardiology, Cardiovascular Research Institute Maastricht, Faculty of Health, Medicine, and Life Sciences, Maastricht University, PO Box 616, 6200 MD Maastricht, The Netherlands; 2Department of Medicine, Montreal Heart Institute and Universit de Montral, Montreal, Canada; 3Department of Pharmacology and Therapeutics, McGill University, Montreal, Canada; 4Institute of Pharmacology, West German Heart and Vascular Center, Faculty of Medicine, University Duisburg-Essen, Duisburg, Germany; 5IHU Liryc and Fondation Bordeaux Universit, Bordeaux, France; 6Alliance for Cardiovascular Diagnostic and Treatment Innovation, and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA; and 7Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA  \nReceived 31 December 2020; editorial decision 17 March 2021; online publish-ahead-of-print 23 April 2021  \nAbstract Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly  \nprevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.  \n􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂 􀀂  \n* Corresponding author. Tel: þ31 43 38 76046, E-mail: [jordi.heijman@maastrichtuniversity.nl](jordi.heijman@maastrichtuniversity.nl)  \n The Author(s) 2021. Published by Oxford University Press on behalf of the European Society of Cardiology.  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ([http://creativecommons.org/licens","cbCaihkSKp328Q9b","https://ap.wps.com/l/cbCaihkSKp328Q9b","pdf",1792800,18,"English","# Introduction\n## Clinical burden and current AF management framework\n## Role of computational modelling in AF research and care\n## Narrative review scope and structure","[{\"question\":\"Why is early identification of causal factors in atrial fibrillation challenging?\",\"answer\":\"AF involves diverse mechanisms for initiation, maintenance and progression, and its clinical presentation varies widely. This complexity makes early detection of modifiable causal factors with therapeutic interventions difficult.\"},{\"question\":\"How does computational modelling contribute to atrial fibrillation research and clinical care?\",\"answer\":\"Computational modelling integrates multiple datasets across levels, improving mechanistic understanding. It also supports risk prediction and personalized therapy through mathematical simulations of cardiac electrophysiology.\"},{\"question\":\"What does the narrative review focus on regarding computational models in AF management?\",\"answer\":\"The review summarizes clinical challenges that could benefit from computational modelling, outlines the main in silico approaches and their achievements, and discusses major limitations that hinder routine clinical use. It concludes with future perspectives.\"}]","Computational models of atrial fibrillation - achievements, challenges, and perspectives for improving clinical care | PDF",1791086373,45]