Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care.
Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care.
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DOI：
10.1093/cvr/cvab138
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发表时间：
2021-06-16
影响因子：
10.8
通讯作者：
Trayanova NA
中科院分区：
文献类型：
--
作者：
Heijman J;Sutanto H;Crijns HJGM;Nattel S;Trayanova NA
尽管在其检测、理解和管理方面取得了重大进展，但心房颤动（AF）仍然是一种高度流行的心律失常，对数百万患者的发病率和死亡率产生重大影响。房颤是由风险因素和合并症之间复杂的动态相互作用引起的，合并症可诱导不同的心房重构过程。心房重构增加AF的脆弱性和持续性，同时促进疾病进展。房颤的发生、维持和进展所涉及的表现和广泛机制的变异性及其相关不良结局使得早期识别可通过治疗干预进行改变的因果因素具有挑战性，可能导致当前房颤管理的疗效不佳。计算建模促进了多个数据集的多层次整合，并为机械理解，风险预测和个性化治疗提供了新的机会。心脏电生理学的数学模拟已经存在了60年，并且越来越多地用于提高我们对AF机制的理解并指导AF治疗。这种叙述性的审查重点是新兴的和未来的应用程序的计算模型在AF管理。我们总结了可能受益于计算建模的临床挑战，概述了不同的计算机模拟方法及其显著成就，并讨论了阻碍这些方法常规临床应用的主要限制。最后，展望未来。随着包括计算在内的电子技术的快速发展，计算建模的临床应用正在迅速发展。我们预计，它们的应用将逐步增加，特别是如果它们的附加值可以在临床试验中得到证明。
Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent 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.
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影响因子：
4
作者：
Boyle PM;Hakim JB;Zahid S;Franceschi WH;Murphy MJ;Vigmond EJ;Dubois R;Haïssaguerre M;Hocini M;Jaïs P;Trayanova NA;Cochet H
通讯作者：
Cochet H
影响因子：
6.1
作者：
Boyle, Patrick M.;Zahid, Sohail;Trayanova, Natalia A.
通讯作者：
Trayanova, Natalia A.
影响因子：
4
作者：
Boyle PM;Hakim JB;Zahid S;Franceschi WH;Murphy MJ;Prakosa A;Aronis KN;Zghaib T;Balouch M;Ipek EG;Chrispin J;Berger RD;Ashikaga H;Marine JE;Calkins H;Nazarian S;Spragg DD;Trayanova NA
通讯作者：
Trayanova NA
影响因子：
168.9
作者：
Attia, Zachi, I;Noseworthy, Peter A.;Friedman, Paul A.
通讯作者：
Friedman, Paul A.
影响因子：
37.8
作者：
Aguilar, Martin;Xiong, Feng;Nattel, Stanley
通讯作者：
Nattel, Stanley