Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models

TLDR

Sudden cardiac death from arrhythmias is a leading cause of mortality, and while ICD implantation reduces risk, current risk‑stratification methods lack sensitivity and specificity, resulting in few appropriate ICD therapies. The study aims to develop a personalized imaging‑based computational method to assess sudden cardiac death risk in post‑MI patients. The method builds patient‑specific 3‑D cardiac models from MRI data and evaluates each model’s arrhythmia propensity. In a retrospective proof‑of‑concept study, the virtual heart test outperformed existing clinical metrics in predicting arrhythmic events and could prevent SCD while reducing unnecessary ICD implantations.

Abstract

Abstract 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.