Artificial intelligence electrocardiogram as a novel screening tool to detect and longitudinally monitor high-risk echocardiographic features in hypertrophic cardiomyopathy
Year: 2025
Type: article
Abstract: Background: Current practice protocols for hypertrophic cardiomyopathy (HCM) recommend integrating crucial high-risk echocardiographic features associated with adverse prognosis into patient management. However, echocardiography is resource-limited and few powerful screening tools are available for risk assessment in the community. We aimed to devise an artificial intelligence (AI)-electrocardiogram (ECG) to identify high-risk echocardiographic features and monitor feature changes during long-term follow-up. Methods: Patients with HCM from two hospitals who underwent both ECG and echocardiography within a 14-day window were retrospectively identified. One site (n=2,591) was used for training, validation and testing, and the other site (n=171) was used for external validation. An AI-ECG model was trained to predict the presence of any of the following four high-risk echocardiographic features: resting left ventricular outflow tract obstruction (LVOTO), massive left ventricular hypertrophy (LVH), systolic dysfunction, and apical aneurysm. Results: The AI-ECG model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.81 [95% confidence interval (CI): 0.76-0.86] and 0.80 (95% CI: 0.72-0.87) in the testing and external validation set for identifying high-risk features. During a median follow-up of 37 months (interquartile range, 30-53 months), in the testing set, 30 patients with no high-risk features at baseline were evaluated. Ten patients developed new high-risk features, and changes in the predicted probability score was well matched to the trend of changes in risk status. Patients with false-positive results at baseline had a threefold increased risk of developing high-risk features compared with the true-negatives [hazard ratio (HR) 3.36, 95% CI: 0.77-14.65; P=0.037]. Conclusions: The AI-ECG model effectively identified and longitudinally monitored high-risk echocardiographic features and may serve as a powerful community screening tool.
Authors: Hongxia Ning, Qi Yu, Fanxin Kong, Guangyuan Li, Yonghuai Wang, Bo Zhang, Yong Feng, Chunyan Ma
Language: English
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4409018267