Global Electric Heterogeneity Risk Score for Prediction of Sudden Cardiac Death in the General Population The Atherosclerosis Risk in Communities (ARIC) and Cardiovascular Health (CHS) Studies

Background-Asymptomatic individuals account for the majority of sudden cardiac deaths (SCDs). Development of effective, low-cost, and noninvasive SCD risk stratification tools is necessary. Methods and Results-Participants from the Atherosclerosis Risk in Communities study and Cardiovascular Health Study (n= 20 177; age, 59.3+/-10.1 years; age range, 44-100 years; 56% female; 77% white) were followed up for 14.0 years (median). Five ECG markers of global electric heterogeneity (GEH; sum absolute QRST integral, spatial QRST angle, spatial ventricular gradient [SVG] magnitude, SVG elevation, and SVG azimuth) were measured on standard 12-lead ECGs. Cox proportional hazards and competing risks models evaluated associations between GEH electrocardiographic parameters and SCD. An SCD competing risks score was derived from demographics, comorbidities, and GEH parameters. SCD incidence was 1.86 per 1000 person-years. After multivariable adjustment, baseline GEH parameters and large increases in GEH parameters over time were independently associated with SCD. Final SCD risk scores included age, sex, race, diabetes mellitus, hypertension, coronary heart disease, stroke, and GEH parameters as continuous variables. When GEH parameters were added to clinical/demographic factors, the C statistic increased from 0.777 to 0.790 (P= 0.008), the risk score classified 10-year SCD risk as high (> 5%) in 7.2% of participants, 10% of SCD victims were appropriately reclassified into a high-risk category, and only 1.4% of SCD victims were inappropriately reclassified from high to intermediate risk. The net reclassification index was 18.3%. Conclusions-Abnormal electrophysiological substrate quantified by GEH parameters is independently associated with SCD in the general population. The addition of GEH parameters to clinical characteristics improves SCD risk prediction.