Real-World Application of Deep Learning for ECG-Based Prediction of Coronary Artery Disease and Revascularization Needs

Background Early detection of the need for coronary revascularization and timely intervention may reduce fatal events, but limited screening tools often leads to underdiagnosis. Objective The aim of this study is to use a deep learning model (DLM) that utilizes electrocardiography (ECG) and the eXtreme Gradient Boosting (XGBoost) model to predict risk of coronary revascularization in the general population. Methods This study included patients with at least one ECG per patient. The development set comprised 113,451 patients for training a DLM. After excluding patients with elevated troponin I levels and those without follow-up records, the internal validation set consisted of 66,680 patients. The external validation was conducted using data from a community hospital. XGBoost predicted events based on demographic data and ECG features. The primary endpoint was coronary revascularization within one year. Model performance was evaluated using the C-index. Results The DLM stratified patients by risk of coronary revascularization within one year. The study included 51% males with a mean age of 53 years, 10% with diabetes, and a revascularization rate of 2.6%. High-risk patients had a hazard ratio of 9.77 (95% CI: 7.63-12.51) compared to low-risk patients. The C-index was 0.825 (95% CI: 0.81–0.84). Combining demographic and AI-ECG data, XGBoost achieved a C-index of 0.884 (95% CI: 0.87–0.89). Comparative C-index analysis revealed significantly different discriminative performance between models (p = 1.110223e-15). Conclusions The DLM demonstrates ECG's potential as a screening tool for coronary revascularization, enabling opportunistic detection and prompting further evaluation of high-risk patients.