Abstract 11648: ECG-Derived Features Enable Prediction of Sudden Death in Ischemic Cardiomyopathy

Introduction: Low left ventricular ejection fraction (LVEF) is an imperfect predictor of sudden cardiac death (SCD) in patients with ischemic cardiomyopathy. Novel features identified via automated ECG analysis might improve the prediction of sudden cardiac death risk. Hypothesis: We hypothesized that machine learning of the ECG can be used to predict SCD. Methods: We studied 5603 ECG Lead V1 beats in 41 patients (64±10 Y) with coronary disease and LVEF≤40% in steady-state pacing. Patients were randomly allocated to independent training and test cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines were trained to predict mortality at 3Y from the top 20 features derived from these beats (Fig C). Patient-level predictions were made by computing an ECG score that indicates the proportion of test set beats in that patient computed by the beat-level model to predict SCD. Results: Fig A shows the data flow in the study. Beat-level predictions in the validation (n=1678 Lead I beats) cohorts yielded c-statistics of 0.78 for SCD (95% CI, 0.62-0.91). In multivariable models, c-statistic was 0.87 for SCD (95% CI, 0.76-0.98) (Fig B). Conclusions: Machine learning of the ECG reveals novel predictors of SCD risk in patients with ischemic cardiomyopathy. This tool may improve risk stratification and allocation for ICD therapy beyond LVEF alone.