Time-Dependent ECG-AI Prediction of Fatal Coronary Heart Disease

Background Sudden cardiac death (SCD) affects >4 million people globally, and ∽300,000 yearly in the US. Fatal coronary heart disease (FCHD) is used as a proxy to SCD when coronary disease is present and no other causes of death can be identified. Electrocardiographic (ECG) artificial intelligence (AI) models (ECG-AI) show promise in predicting adverse coronary events yet their application to FCHD is limited. Objectives This research aimed to develop accurate ECG-AI models to predict risk for FCHD within the general population using waveform 12- and single-lead ECG data as well as assess time-dependent risk. Methods Standard 10-second 12-lead ECGs sampled at 250Hz, demographic and clinical data from University of Tennessee Health Science Center (UTHSC) were used to develop and validate models. Eight models were developed and tested: two classification models with convolutional neural networks (CNN) using 12- and single-lead ECGs as inputs (12-ECG-AI and 1-ECG-AI, respectively) and six time- dependent cox proportional hazard regression (CPHR) models using demographics, clinical data and ECG-AI outputs. The dataset was split into 80% for model derivation, with five-fold cross-validation, and 20% holdout test set. Models were evaluated using the AUC and C-Index. Correlation of predicted risks from the 12-lead (12-ECG-AI) and single-lead (1-ECG-AI) CNN models was assessed. Results A total of 50,132 patients were included in this study (29,093 controls and 21,039 cases) with a total of 167,662 ECGs with mean age of 62.50±14.80years, 53.4% males and 48.5% African-Americans. The 12- and 1-ECG-AI models resulted AUCs=0.77 and 0.76, respectively on the holdout data. The best performing model was C12-ECG-AI-Cox (demographics+clinical+ECG) with no time restriction AUC=0.85(0.84-0.86) and C-Index= 0.78(0.77-0.79). 2-year FCHD risk prediction reached AUC=0.91(0.90-0.92). The 12-/1-ECG-AI models’ predictions were highly correlated (R 2 = 0.72). Conclusion 2-year risk for FCHD can be predicted with moderate accuracy from ECG data alone. When combined with other data, a very high accuracy was obtained. High correlation between single-lead and 12-lead ECG models infer opportunities for screening larger patient populations for FCHD risk.