Purpose: To develop sex-specific lifetime risk prediction models using electrocardiographic (ECG) global electrical heterogeneity (GEH) and clinical characteristics.
Methods: Participants from the Atherosclerosis Risk in Communities study with analyzable ECGs (n=14,725; age, 54.2±5.8 yrs; 55% female, 74% white) were followed up for 24.4 years (median). Traditional ECG and GEH variables were measured on 12-lead ECGs. A Cox regression model was used to develop a prediction model. In women, the final model included race, age, coronary heart disease (CHD), stroke, hypertension, diabetes, smoking, high-density lipoprotein, albumin, uric acid, education level, heart rate, QTc, sum absolute QRST integral, spatial peak QRS-T angle. In men, the final prediction model included age, race, CHD, stroke, hypertension, diabetes, total cholesterol, physical activity, smoking, serum phosphorus, albumin, chronic kidney disease, spatial area QRS-T angle, area spatial ventricular gradient (SVG) elevation and magnitude, and peak SVG magnitude.
Results: There were a total of 530 SCDs. Our prediction models showed robust prediction of SCD in both sexes [(Harrell’s C-statistic women 0.863 (95% CI 0.845-0.882), men 0.786 (95%CI 0.786-0.803)]. In women when ECG and GEH variables were added to clinical variables, the net reclassification improved by 9% (P=0.001) (Table). In men there was no significant reclassification improvement.
Conclusions: We were the first to develop sex-specific lifetime SCD prediction models. The addition of ECG GEH to clinical variables improved SCD risk reclassification in women, but not in men. Prediction of SCD was more accurate in women as compared to men.