This study aims to develop and validate a deep learning-based electrocardiogram (ECG) model for predicting the future risk of heart failure with reduced ejection fraction (HFrEF). The model is trained using raw 12-lead ECG data and generates individualized, time-resolved risk estimates over a 5-year period.
Data are obtained from multiple cohorts, including Zhongshan Hospital, Shanghai Tenth People's Hospital, and Beth Israel Deaconess Medical Center, representing diverse populations across China and the United States. The model is designed to identify individuals at elevated risk of developing HFrEF before the onset of overt clinical disease.
The performance of the model is evaluated using multiple complementary metrics, including discrimination, calibration, and clinical utility. In addition, interpretability analyses are conducted to explore the physiological relevance of ECG features associated with predicted risk.
This study seeks to provide an accessible and scalable tool for early risk stratification of heart failure, with the potential to support timely clinical decision-making and improve patient outcomes.