Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models.
Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models.
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DOI：
10.1038/ncomms11437
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发表时间：
2016-05-10
影响因子：
16.6
通讯作者：
Trayanova NA
中科院分区：
文献类型：
--
作者：
Arevalo HJ;Vadakkumpadan F;Guallar E;Jebb A;Malamas P;Wu KC;Trayanova NA
心律失常导致的心脏性猝死(SCD)是导致死亡的主要原因。对于SCD高危患者，预防性植入植入性心律转复除颤器(ICD)可降低死亡率。然而，目前识别有心律失常风险的患者的方法敏感性和特异性都很低，这导致适当的ICD治疗的比率很低。在这里，我们开发了一种基于心脏成像和计算模型的个性化方法来评估脑梗塞后患者的SCD风险。我们从患者的临床磁共振成像数据中构建了个性化的梗死后心脏三维计算机模型，并评估了每个模型发生心律失常的倾向。在一项概念验证的回溯性研究中，虚拟心脏测试在预测未来心律失常事件方面显著优于现有的几种临床指标。稳健和非侵入性的个性化虚拟心脏风险评估可能有可能预防SCD和避免不必要的ICD植入。心律失常猝死是导致死亡的主要原因，然而，识别高危患者的方法敏感性和特异性都较低。在这里，作者开发了一种个性化的方法来评估梗死后患者的心律失常风险，该方法基于心脏成像和计算模型，显著优于现有的临床指标。
Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients' clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations. Sudden arrhythmic death is a leading cause of mortality, however approaches to identify at-risk patients are of low sensitivity and specificity. Here, the authors develop a personalized approach to assess arrhythmia risk in post-infarction patients based on cardiac imaging and computational modelling that significantly outperforms existing clinical metrics.