Abstract 4371172: Artificial Intelligence-based screening for Hypertrophic Cardiomyopathy from Single-lead Electrocardiograms: A Multinational Development and Validation Study

作者：Philip M. Croon, Arya Aminorroaya, Aline F Pedroso, Lovedeep Singh Dhingra, Rohan Khera · 发表于：Circulation · 年份：2025 · DOI：10.1161/circ.152.suppl_3.4371172 · 被引用次数：1 · 研究领域：ECG Monitoring and Analysis、Cardiovascular Health and Risk Factors、Cardiovascular Effects of Exercise

Background: Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden death in young and middle-aged adults and frequently goes undiagnosed due to the absence of accessible and scalable screening strategies. Artificial intelligence (AI) applied to single-lead electrocardiograms (ECGs) from portable devices offers a promising approach for large-scale screening. However, noisy signals can substantially compromise diagnostic accuracy. Aim: We developed and validated a noise-adapted AI-ECG model specifically designed to detect HCM from noisy single-lead ECGs. Methods: We developed an AI-ECG model using lead I from 160,396 unique 12-lead ECGs of 85,967 individuals in the Yale New Haven Health System (YNHHS) and augmented the ECG signal with real-world noises to develop noise-resilient models. A held-out test including 38,426 ECGs from unique individuals (mean age of 53.9 ± 19.3 years, 20,309 [53%] women) was used for internal validation. There were 59 (0.2%) HCM cases, adjudicated by expert clinicians using cardiac magnetic resonance (CMR) imaging. External validation was performed in manually validated MIMIC-IV (n=995, 66 HCM cases) and the UK Biobank (n=57,963, 53 HCM cases). To assess model fairness, we conducted stratified analyses by age, sex, race/ethnicity, and key ECG features. Results: The model demonstrated robust discrimination in internal validation with an area under the receiver operating curve (AUROC) of 0.95 (95% CI 0.93-0.97), sensitivity of 0.90 and specific...