Clinical Electrophysiology -> SCA Risk Assessment: -> Other Noninvasive Techniques D-AB10 - Ventricular Arrhythmias from Mechanism to New Ablation Targets (ID 52) Abstract Plus

D-AB10-03 - Personalized Virtual Heart Technology Identifies Ventricular Tachycardia Risk In Patients With Hypertrophic Cardiomyopathy (ID 736)

Disclosure
 R.P. O'Hara: Nothing relevant to disclose.

Abstract

Background: Hypertrophic cardiomyopathy (HCM), a disease of progressive myocardial fibrosis, is the most common cause of sudden cardiac death (SCD) in the young and is a significant cause of SCD in adults. Patients with HCM are typically asymptomatic but can develop ventricular tachycardia (VT), which can lead to SCD. Current risk stratification criteria (ACCF/AHA, ESC) are inadequate in identifying patients at risk for VT and in need of an implantable cardioverter defibrillator (ICD). Previously, virtual heart technology has successfully stratified SCD risk in the ischemic population.
Objective: To demonstrate that HCM-specific virtual heart technology improves SCD risk stratification compared to current clinical practice.
Methods: In this retrospective study, we reconstructed 3D left ventricular models (Fig A) using clinical data from 20 HCM patients with ICDs, but with only 10 experiencing VT. We incorporated a fusion of contrast-enhanced LGE-MRI and T1 mapping to define regions of non-fibrotic myocardium as well as diffuse and dense fibrosis at the tissue level. At the cellular level, we incorporated HCM-specific cell remodeling (Fig B). Rapid pacing was used to assess VT inducibility.
Results: The distributions of fibrotic remodeling and hypertrophy were not significantly associated with clinical VT (Fig C). Our virtual heart technology was more accurate with a greater sensitivity and specificity (70%, 80%, 60%) compared to clinical VT risk assessment (45%, 50%, 40%) (Fig D).
Conclusion: Personalized heart technology improves the identification of patients with HCM at risk of VT compared to current clinical risk criteria, thus improving SCD risk stratification.
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