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.
Highlights
Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality
To determine the level of SCD risk, clinical cardiology practice relies on a ‘one-sizefits-all’ metric representing a global reduction in ventricular function: the left ventricular ejection fraction (LVEF)o35% metric[3]
Previous research[11] has indicated that the presence of infarct border zone (termed grey zone (GZ) because of its intermediate signal intensity in clinical magnetic resonance imaging (MRI)) contributes to arrhythmia propensity, pixels belonging to infarcted tissue are further sub-classified as scar or GZ
Summary
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. We develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We conduct a virtual multisite delivery of electrical stimuli from ventricular locations at different distances to remodelled tissue so that the patient’s heart propensity to develop infarct-related ventricular arrhythmias can be comprehensively evaluated. We term this non-invasive SCD risk assessment approach VARP, a Virtual-heart Arrhythmia Risk Predictor. The robust and non-invasive VARP approach may have the potential to prevent SCD and avoid unnecessary ICD implantations in post-infarction patients
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