Electrocardiogram-Based Deep Learning for Time-Resolved Prediction of Heart Failure With Reduced Ejection Fraction: A Multinational Study
TL;DR
This ECG-based deep learning survival model provides individualized, time-resolved, and clinically interpretable estimates of future HFrEF risk with robust performance across multinational cohorts and supports the potential of AI-enabled ECG analysis as an accessible tool for early HFrEF risk stratification within routine clinical workflows.
Abstract
Background: Heart failure with reduced ejection fraction (HFrEF) remains a major global health burden. Most electrocardiogram (ECG)-based artificial intelligence models are limited to diagnostic tasks or fixed-horizon prognostic classification and provide little insight into the temporal evolution of risk. In addition, concerns regarding model interpretability continue to impede clinical adoption. Whether deep learning applied to ECGs can deliver individualized, time-resolved, and biologically interpretable risk estimates for incident HFrEF across diverse populations remains uncertain. Methods: We developed a convolutional neural network-based survival model using raw 12-lead ECGs from Zhongshan Hospital (SHZS) and externally validated it in independent cohorts from Shanghai Tenth People's Hospital (SHTP) and Beth Israel Deaconess Medical Center (BIDMC). The model generated individualized, day-by-day probabilities of incident HFrEF over a 5-year horizon. Performance was comprehensively evaluated using discrimination, calibration, precision-recall characteristics, clinical utility, and risk stratification metrics, with subgroup analyses across age, sex, and race to assess generalizability. Model interpretability was examined using complementary representation and attention-based frameworks. Results: In 458,884 patients, the survival model demonstrated strong and stable discrimination across cohorts, with overall C-indices of 0.971 (95% CI, 0.965-0.976) in SHZS, 0.945 (95% CI, 0.938-0.950) in SHTP, and 0.855 (95% CI, 0.850-0.860) in BIDMC, and consistently high time-dependent AUROC values across the 1-5-year horizons. Calibration showed close agreement between predicted and observed risks, and decision curve analyses indicated meaningful net clinical benefit across a broad range of thresholds. Kaplan-Meier curves showed clear stratification across predicted risk groups. Interpretability analyses identified physiologically coherent ECG features related to QRS duration, heart rate, and QT interval that were associated with predicted risk. Conclusion: This ECG-based deep learning survival model provides individualized, time-resolved, and clinically interpretable estimates of future HFrEF risk with robust performance across multinational cohorts. These findings support the potential of AI-enabled ECG analysis as an accessible tool for early HFrEF risk stratification within routine clinical workflows.