Estimation of Left Ventricular Systolic Function in Pediatric and Congenital Heart Disease from Serial...

Estimating left ventricular ejection fraction (LVEF) from electrocardiograms (ECGs) is a useful task enabled by artificial intelligence (AI-ECG). Prior work focuses on predicting LVEF from single ECGs. Here, we investigate whether a patient's history of ECGs can improve LVEF prediction. We study a longitudinal cohort from Boston Children's Hospital, deriving LVEF from echocardiograms conducted within 2 days of ECGs (n=178,495, n. patients: 70,226, median age: 10.6). We propose a sequential AI-ECG approach using convolutional layers to represent single ECGs and a sequential neural network to reason over longitudinal ECGs. We build and test several sequential architectures. For predictions with at least 5 previous ECGs, sequential AI-ECG improved median AUROC (IQR) by 3.4 points (1.4, 4.4). When predicting the LVEF value, sequential AI-ECG improves Pearson R by 0.08 (0.02, 0.13). Results suggest that patients' longitudinal ECG history contains valuable information for improving AI-ECG risk stratification beyond current snapshot-based models.