Abstract
Hypertrophic cardiomyopathy (HCM) is associated with risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VAs) arising from the proliferation of fibrosis in the heart. Current clinical risk stratification criteria inadequately identify at-risk patients in need of primary prevention of VA. Here, we use mechanistic computational modeling of the heart to analyze how HCM-specific remodeling promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients. We combine contrast-enhanced cardiac magnetic resonance imaging and T1 mapping data to construct digital replicas of HCM patient hearts that represent the patient-specific distribution of focal and diffuse fibrosis and evaluate the substrate propensity to VA. Our analysis indicates that the presence of diffuse fibrosis, which is rarely assessed in these patients, increases arrhythmogenic propensity. In forecasting future VA events in HCM patients, the imaging-based computational heart approach achieved 84.6%, 76.9%, and 80.1% sensitivity, specificity, and accuracy, respectively, and significantly outperformed current clinical risk predictors. This novel VA risk assessment may have the potential to prevent SCD and help deploy primary prevention appropriately in HCM patients.
Highlights
Post-contrast T1 mapping, a parametric imaging modality, has been used to visualize diffuse fibrosis in patients with Hypertrophic cardiomyopathy (HCM).(Chu et al, 2017; Ellims et al, 2012) We have previously developed a computational modeling approach to predict sudden cardiac death (SCD) risk due to ventricular arrhythmias (VA) in post-infarction patients,(Arevalo et al, 2016) We hypothesized that a new personalized virtual-heart technology, one that entails constructing fusion electrophysiological models based on the distribution of both dense and diffuse fibrosis, as acquired by the two different cardiac magnetic27 resonance (CMR)
In a proof-of-concept patient cohort, we assess the predictive capability of the approach as compared to that of other clinical metrics for VA risk prediction in Results: The new approach to analyzing arrhythmogenic propensity in HCM patients developed here involved creating three-dimensional (3D) patient-specific electrophysiological ventricular models based on fusing data from late gadolinium enhancement (LGE)-CMR and post-contrast T1 mapping
VA inducibility in each HCM patient’s substrate was probed to determine VA risk for the patient and to understand the mechanisms of arrhythmogenesis, and the contribution of the individualized diffuse fibrosis distribution, which is rarely assessed in these patients
Summary
Hypertrophic cardiomyopathy (HCM) is the most common cause of sudden cardiac death (SCD) in the young and is a significant cause of sudden death in adults.(Maron, 2004) The disease, with an incidence of 1 in 500, presents with progressive myocardial fibrosis which can create substrates for ventricular arrhythmias (VA) leading to SCD in patients who are typically asymptomatic.(Galati et al, 2016; Olivotto et al, 2012) Implantable cardioverter defibrillator (ICD) deployment, a procedure that carries risk of potential complications and morbidity, is used as primary prevention of SCD due to VA in patients with HCM.(Lambiase et al, 2016; Jayatilleke et al, 2004) current risk stratification criteria outlined by the AmericanCollege of Cardiology Foundation/American Heart Association (ACCF/AHA) and EuropeanSociety of Cardiology (ESC) fail to accurately identify all patients at risk for SCD, leading to suboptimal rates of appropriate ICD implantation.(Gersh et al, 2011; O'Mahony et al, 2014; Schinkel et al, 2012) many HCM patients receive ICDs without deriving any health benefits, while others are not adequately protected. Post-contrast T1 mapping, a parametric imaging modality, has been used to visualize diffuse fibrosis in patients with HCM.(Chu et al, 2017; Ellims et al, 2012) We have previously developed a computational modeling approach (virtual heart) to predict SCD risk due to VA in post-infarction patients,(Arevalo et al, 2016) We hypothesized that a new personalized virtual-heart technology, one that entails constructing fusion electrophysiological models based on the distribution of both dense and diffuse fibrosis, as acquired by the two different CMR modalities, would be predictive of the propensity of the HCM-remodeled substrate to VAs and could be used to assess SCD risk due to VA in this patient population. In a proof-of-concept patient cohort, we assess the predictive capability of the approach as compared to that of other clinical metrics for VA risk prediction in HCM
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