How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients.
How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients.
复制标题
个性化的心脏建模如何有助于治疗致命性心律失常：关注肌肉后患者的心室心动过速消融策略。
DOI：
10.1002/wsbm.1477
复制
发表时间：
2020-05
期刊：
影响因子：
--
通讯作者：
Prakosa A
中科院分区：
文献类型：
--
作者：
Trayanova NA;Doshi AN;Prakosa A
精密心脏病学是一种针对心血管疾病预防和治疗的策略，可解释个体差异。计算心脏建模是在精密心脏病学的保护伞下开发的新方法之一。患者心脏的个性化计算建模在模型的开发方面取得了长足的进步，该模型结合了心脏的个体几何形状和结构以及其他患者特定信息。在这些发展中，潜在的最有影响力的研究之一是旨在使用患者特定模型无创预测致命性心律失常、室性心动过速（VT）消融的靶点。在概念验证研究中，该方法已成功应用于缺血性心肌病患者。本文的目的是回顾缺血性心肌病患者的计算室性心动过速消融指导策略，从模型开发到实际临床应用的复杂性。为了提供上下文描述的道路，这些计算建模的应用程序进行，我们首先回顾了最先进的室性心动过速消融在临床上，强调的好处，个性化的计算预测的消融目标可以带来临床电生理实践。
Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient-specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient-specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof-of-concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice.
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影响因子：
3.8
作者：
Bayer, J. D.;Blake, R. C.;Plank, G.;Trayanova, N. A.
通讯作者：
Trayanova, N. A.
影响因子：
4.6
作者：
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通讯作者：
SPACH, MS
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作者：
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通讯作者：
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1.6
作者：
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通讯作者：
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