医学
肥厚性心肌病
心电图
心脏病学
心肌病
鉴定(生物学)
内科学
深度学习
人工智能
心力衰竭
植物
计算机科学
生物
作者
Richard Carrick,Hisham Ahamed,Eric Sung,Martin S. Maron,Christopher Madias,Vennela Avula,Rachael Studley,Changchun Bao,Nadia Bokhari,Erick Quintana,Ramiah Rajeshkannan,Barry J. Maron,Kathérine C. Wu,Ethan J. Rowin
标识
DOI:10.1016/j.hrthm.2024.01.031
摘要
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.To develop electrocardiogram (ECG) deep-learning (DL) models for identification of HCM patients with high-risk features.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).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%.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.
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