Abstract 4136938: Autonomous Personalized Dynamic ECG Analysis Predicts 1-hour Incidence of Sudden Cardiac Arrest

Abstract

Background: Sudden cardiac arrest (SCA) is a leading cause of death in the USA. Defibrillators prevent SCA by delivering shocks for sustained ventricular tachyarrhythmias (VT). However, current clinical methods for predicting SCA are limited. We have shown that distinct nonlinear fluctuation patterns “hidden” within normal-appearing ECG waveforms have unique prognostic value. Hypothesis: Autonomous personalized dynamic ECG analysis predicts the 1-hour incidence of SCA. Methods: In an established pressure-overload model that exhibits key features of human VT and SCA (e.g., cardiac restitution properties), 30% of 37 freely ambulating animals had spontaneous SCA in 4 weeks. The exposures included a rolling 1-hour window of time, frequency and nonlinear domain measures of heart rate and QT time series during sinus rhythm, and EntropyX QT , a higher-dimensional machine learning-based nonlinear measure of cardiac repolarization dynamics. Results: In multivariate analyses, the major independent predictors of 1-hour incidence of SCA were (1) high EntropyX QT ; (2) high parasympathetic tone, as indexed by the high-frequency component of the heart rate power spectral density; and (3) the degree of coupling between RR and QT intervals. The baseline values of EntropyX QT and parasympathetic tone increased by 3.6-fold and 46.5-fold, respectively, during the hour preceding SCA. The adjusted ROC curve area was > 89%. Conclusion: Continuous autonomous analysis of cardiac repolarization and autonomic dynamics strongly and independently predicts the subacute incidence of SCA. Incorporation of these measures into current strategies of SCA risk stratification has potential to save many lives and warrants further evaluation.