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

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.

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).

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.