Machine Learning Based Analysis of Longitudinal Changes in the Congenital Heart Disease Electrocardiogram

PROJECT SUMMARY Delayed intervention for congenital heart defect residua and sequelae can lead to heart failure and end-organ damage. Identifying the optimal time for intervention to avoid both adverse outcomes and to minimize the number of interventions over a lifetime for any given heart defect relies on routine surveillance with expensive

imaging and centralized expertise. Data from the widely available electrocardiogram (ECG) can be transferred from point-of-care to remote data analysis centers. Machine learning and time-series analysis of ECG waves related to atrial depolarization, ventricular depolarization and ventricular repolarization, conduction intervals

and waveform durations can consistently calculate parameters that can be tracked as biomarkers longitudinally. ECG patterns may reflect dysrhythmia, ischemia, hypertrophy, chamber dilatation, ventricular fibrosis and dysfunction, and can change over time in response to subclinical changes in the cardiac

chambers. A significant problem in detecting subtle changes in the ECG is the reliance on normal intervals and pattern descriptions that lack nuance to detect longitudinal changes on an individual basis that may reflect impending ventricular failure. We propose to apply artificial intelligence-based methods to analyze longitudinal

ECG changes by age, sex, race and ethnicity in an adult congenital heart disease population. Once we identify and characterize ECG changes over time, we will use the change in ECG parameters to develop a machine learning algorithm to predict the need for cardiac intervention or occurrence of adverse events.