Novel ECG Measures and Risk of Sudden Cardiac Death

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Sudden cardiac death (SCD) is a major public health concern, accounting for 400,000 deaths in the US each year. Clinical and autopsy studies have consistently demonstrated a predominant, common pathophysiology in Western populations, showing that the most common electrophysiologic mechanism for SCD is ventricular fibrillation (VF) and the most common pathologic substrate is coronary heart disease (CHD). In about half of SCD cases, death is the first clinical manifestation of CHD. Yet risk factors of SCD early in the natural history of conditions predisposing SCD have not been fully identified, and SCD risk stratification strategy in general population has not been developed. ECG is easy available, non-expensive and noninvasive tool, which carries valuable information on electrophysiological properties of the heart. However, traditional analysis of ECG includes very limited assessment of the arrhythmogenic substrate. Recently we developed novel 12-lead ECG SCD risk score, composed of parameters that measure (1) slow discontinued conduction, (2) temporal repolarization lability, and (3) adverse electrical remodeling. Preliminary gender-, race-, and age-matched case-control analysis of the Atherosclerosis In Community (ARIC) study showed a total continuous net reclassification improvement of 86.0% as compared to the Framingham risk score alone. We hypothesize that (1) the SCD ECG risk score, comprised of the mechanistic ECG markers of arrhythmogenic substrate derived in the resting 12-lead ECG analysis accurately stratify persons into high-, intermediate- and low-risk groups and improve classification in comparison to the risk stratification on the basis of traditional CHD risk factors; (2) heritable factors of the novel ECG phenotype are associated with the increased SCD risk. This application bridges a critical gap between understanding of the SCD mechanisms and SCD risk stratification in a general population. We will leverage 2 unique large NIH-funded prospective community-dwelling GWAS cohorts with an available digital 12-lead ECG repository: ARIC and CHS. Baseline digital 12-lead ECG will be analyzed by customized Matlab software in PIs laboratory. The SCD ECG risk score will be developed in ARIC and validated in the CHS cohort. Net reclassification improvement will be assessed. Longitudinal changes in studied ECG parameters over 20 years of follow-up will be evaluated as predictors of cardiac structural and functional phenotype, assessed by echocardiography at the 5th ARIC visit. Our study will identify genes, associated with specific ECG traits, which will lead to novel targets for treatments and in the future will enable SCD prevention.