Dynamic Predictive Accuracy of Electrocardiographic Biomarkers of Sudden Cardiac Death within a Survival Framework: The Atherosclerosis Risk in Communities (ARIC) study
Abstract Background—The risk of sudden cardiac death (SCD) is known to be dynamic. However, an accuracy of a dynamic SCD prediction is unknown. We aimed to measure dynamic predictive accuracy of ECG biomarkers of SCD and competing non-SCD. Methods—Atherosclerosis Risk In Community study participants with analyzable ECGs in sinus rhythm were included (n=15,716; 55% female, 73% white, age 54.2±5.8 y). ECGs of 5 follow-up visits were analyzed. Global electrical heterogeneity and traditional ECG metrics were measured. Adjudicated SCD was the primary outcome; non-SCD was competing outcome. Time-dependent area under the (receiver operating characteristic) curve (AUC) analysis was performed to assess prediction accuracy of a continuous biomarker in a period of 3,6,9 months, and 1,2,3,5,10, and 15 years, using survival analysis framework. Reclassification improvement as compared to clinical risk factors (age, sex, race, diabetes, hypertension, coronary heart disease, stroke) was measured. Results—Over a median 24.4 y follow-up, there were 577 SCDs (incidence 1.76 (95%CI 1.63-1.91)/1,000 person-years), and 829 non-SCDs [2.55 (95%CI 2.37-2.71)]. Short-term, spatial ventricular gradient (SVG) elevation predicted SCD (AUC 0.706; 95%CI 0.526-0.886), but not a non-SCD. Short-term, upward and more likely forward–directed SVG vector predicted SCD, whereas backward-directed SVG predicted non-SCD. Within the first 3 months after ECG recording, only SVG azimuth improved reclassification of the risk beyond clinical risk factors (18% SCD events reclassified up). Long-term, backward–directed SVG predicted both SCD and non-SCD. Conclusion—Short-term predictors of SCD, non-SCD, and biomarkers of long-term SCD risk differed, reflecting differences in transient vs. persistent SCD substrates.