A hybrid algorithm-based ECG risk prediction model for cardiovascular disease.

Aims: Little is known about the role of electrocardiography (ECG) in the community population independent of physical and laboratory examinations. Thus, this study developed and validated several ECG-based models for cardiovascular disease (CVD) risk assessment, with or without simple questionnaire-based variables.

Methods and results: Using a derivation cohort of 3734 Chinese participants aged ≥40 years, we developed the ECG-based models to predict the risk of developing CVD (comprising fatal and non-fatal coronary heart disease, unstable angina, stroke, and heart failure). Candidate predictors associated with CVD were screened from hundreds of ECG characteristics using a hybrid algorithm. By incorporating the questionnaire-based predictors, we constructed the ECG-questionnaire model. All models were tested in an external validation cohort (n = 1224) to determine their discrimination and calibration. Over a maximum follow-up of 7 years, 433 CVD events occurred in the derivation cohort. The ECG model with 37 selected features achieved comparable performance concerning the clinical model using traditional cardiovascular risk factors (C-statistic: 0.690, 95% confidence interval [CI]: 0.638-0.743) in the external validation cohort. Such performance significantly improved when the questionnaire-based predictors were added (C-statistic: 0.734, 95% CI: 0.685-0.784; calibration χ²: 3.334, P = 0.950). Compared with the clinical model, 17.4% of the participants were correctly assigned to the corresponding risk groups, with an absolute integrated discrimination index of 0.048 (95% CI: 0.016-0.080).

Conclusion: The ECG model with/without questionnaire-based variables can accurately predict future CVD risk independent of physical and laboratory examinations, suggesting its great potential in routine clinical practice.