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计算模型在指导临床心房颤动消融中的研究进展。

Current progress of computational modeling for guiding clinical atrial fibrillation ablation.

机构信息

College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China.

School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China.

出版信息

J Zhejiang Univ Sci B. 2021 Oct 15;22(10):805-817. doi: 10.1631/jzus.B2000727.

DOI:10.1631/jzus.B2000727
PMID:34636185
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8505458/
Abstract

Atrial fibrillation (AF) is one of the most common arrhythmias, associated with high morbidity, mortality, and healthcare costs, and it places a significant burden on both individuals and society. Anti-arrhythmic drugs are the most commonly used strategy for treating AF. However, drug therapy faces challenges because of its limited efficacy and potential side effects. Catheter ablation is widely used as an alternative treatment for AF. Nevertheless, because the mechanism of AF is not fully understood, the recurrence rate after ablation remains high. In addition, the outcomes of ablation can vary significantly between medical institutions and patients, especially for persistent AF. Therefore, the issue of which ablation strategy is optimal is still far from settled. Computational modeling has the advantages of repeatable operation, low cost, freedom from risk, and complete control, and is a useful tool for not only predicting the results of different ablation strategies on the same model but also finding optimal personalized ablation targets for clinical reference and even guidance. This review summarizes three-dimensional computational modeling simulations of catheter ablation for AF, from the early-stage attempts such as Maze III or circumferential pulmonary vein isolation to the latest advances based on personalized substrate-guided ablation. Finally, we summarize current developments and challenges and provide our perspectives and suggestions for future directions.

摘要

心房颤动(AF)是最常见的心律失常之一,与高发病率、高死亡率和高医疗保健费用相关,给个人和社会都带来了巨大的负担。抗心律失常药物是治疗 AF 的最常用策略。然而,由于其疗效有限和潜在的副作用,药物治疗面临挑战。导管消融术被广泛用作 AF 的替代治疗方法。然而,由于 AF 的机制尚未完全了解,消融后的复发率仍然很高。此外,消融的结果在医疗机构和患者之间可能存在显著差异,特别是对于持续性 AF。因此,哪种消融策略是最佳的问题仍远未解决。计算建模具有可重复操作、成本低、无风险和完全可控的优点,不仅是预测不同消融策略在同一模型上的结果的有用工具,也是为临床参考甚至指导寻找最佳个性化消融靶点的有用工具。本综述总结了用于 AF 的导管消融的三维计算建模模拟,从 Maze III 或环形肺静脉隔离等早期尝试到基于个性化底物引导消融的最新进展。最后,我们总结了当前的发展和挑战,并对未来的方向提出了我们的观点和建议。

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Machine Learning in Arrhythmia and Electrophysiology.机器学习在心律失常和电生理学中的应用。
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Left Atrial Appendage Electrical Isolation Reduces Atrial Fibrillation Recurrences: A Simulation Study.左心耳电隔离减少房颤复发:一项模拟研究
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Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation.针对心房颤动的解剖、结构和电基质的左心房消融技术比较。
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Clin Interv Aging. 2020 May 29;15:783-795. doi: 10.2147/CIA.S248170. eCollection 2020.
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Preprocedure Application of Machine Learning and Mechanistic Simulations Predicts Likelihood of Paroxysmal Atrial Fibrillation Recurrence Following Pulmonary Vein Isolation.术前应用机器学习和机制模拟预测肺静脉隔离后阵发性心房颤动复发的可能性。
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