Development and Validation of a Sudden Cardiac Death Prediction Model for the General Population

Abstract

Background Most sudden cardiac death (SCD) events occur in the general population among persons who do not have any prior history of clinical heart disease. We sought to develop a predictive model of SCD among US adults. Methods We evaluated a series of demographic, clinical, laboratory, electrocardiographic, and echocardiographic measures in participants in the Atherosclerosis Risk in Communities (ARIC) Study (n=13,677) and the Cardiovascular Health Study (CHS) (n=4,207) who were free of baseline cardiovascular disease. Our initial objective was to derive a SCD prediction model using the ARIC cohort and validate it in CHS. Independent risk factors for SCD were first identified in the ARIC cohort to derive a 10-year risk model of SCD. We compared the prediction of SCD to non-SCD and all-cause mortality in both the derivation and validation cohorts. Further, we evaluated whether the SCD prediction equation was better at predicting SCD than the 2013 ACC/AHA CVD Pooled Cohort risk equation. Results There were a total of 345 adjudicated SCD events in our analyses, and the 12 independent risk factors in the ARIC study included age, male sex, African American race, current smoking, systolic blood pressure, use of antihypertensive medication, diabetes, serum potassium, serum albumin, HDL, estimated GFR, and QTc interval. Over a 10-year follow-up period, a model combining these risk factors showed good to excellent discrimination for SCD risk (C statistic 0.820 in ARIC and 0.745 in CHS). The SCD prediction model was slightly better in predicting SCD than the 2013 ACC/AHA Pooled Cohort risk equations (C statistic 0.808 in ARIC and 0.743 in CHS). Only the SCD prediction model, however, demonstrated similar and accurate prediction for SCD using both the original, uncalibrated score and the recalibrated equation. Finally, in the echocardiographic subcohort, a left ventricular ejection fraction <50% was present in only 1.1% of participants and did not enhance SCD prediction. Conclusions Our study is the first to derive and validate a generalizable risk score that provides well-calibrated, absolute risk estimates across different risk strata in an adult population of white and African American individuals without a clinical diagnosis of cardiovascular disease.

Epidemiology of Sudden Cardiac Death: Global and Regional Perspectives

“…Family history of SCD may also represent a risk factor for SCD [51]. More recent data suggest other clinical comorbidities may be associated with subsequent SCD, including atrial fibrillation, chronic kidney disease, and obstructive sleep apnoea [52][53][54]. Serious mental illnesses, such as depression, anxiety, and psychosis, have also been linked to SCD [44].…”

Importance of the heart vector origin point definition for an ECG analysis: The Atherosclerosis Risk in Communities (ARIC) study

“…Every day physicians rely on (semi)automatically measured ECG parameters such as corrected QT interval (QTc), and QRS duration to guide clinical decisions. QTc[2] and other ECG metrics[3] were included in the risk models of SCD. Our group recently developed a global electrical heterogeneity (GEH) risk score of SCD[4] and showed that addition of GEH ECG metrics to traditional clinical risk factors significantly improved reclassification of SCD risk.…”

Dynamic Predictive Accuracy of Electrocardiographic Biomarkers of Sudden Cardiac Death within a Survival Framework: The Atherosclerosis Risk in Communities (ARIC) study

“…Recently, we developed an SCD risk score [4], available at www.ecgpredictscd.org. However, our SCD risk model, similarly to other risk models [35,36], includes risk factors that were measured once at baseline.…”