Identification of High-Risk Imaging Features in Hypertrophic Cardiomyopathy using Electrocardiography: A Deep-Learning Approach

BACKGROUND:Patients with hypertrophic cardiomyopathy (HCM) are at risk for sudden death and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter defibrillators. Guidelines recommend cardiac magnetic resonance imaging (CMR) to identify high-risk imaging features. However, CMR is resource intensive and is not widely accessible worldwide. OBJECTIVE:To develop electrocardiogram (ECG) deep-learning (DL) models for identification of HCM patients with high-risk features. METHODS:HCM patients evaluated at Tufts Medical Center (N=1,930; Boston, United States) were used to develop ECG-DL models for prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30mm), apical aneurysm, and extensive late-gadolinium enhancement (LGE). ECG-DL models were externally validated in an HCM cohort from Amrita Hospital (N=233; Kochi, India). RESULTS:ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive LGE) during hold-out model testing (c-statistics 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistics 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy employing echocardiography combined with ECG-DL guided selective CMR use demonstrated sensitivity of 97% for identifying patients with high-risk features, while reducing the number of recommended CMRs by 61%. Negative predictive value with this screening strategy for absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%. CONCLUSIONS:In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in under-resourced areas.