Abstract 14647: EchoNext: An ECG-Based Deep Learning Model to Detect Structural Heart Disease

Abstract

Introduction: Early detection of structural heart diseases (SHD) can help improve patient outcomes. Electrocardiogram (ECG) based deep learning (DL) models have the potential to identify patients (pts) with undiagnosed disease in need of further testing such as echocardiography. Most models have focused on detecting a single abnormality. A few studies have attempted detection on a comprehensive aggregate set of outcomes, but with limited scope and external validation. Hypothesis: An ECG DL model can identify pts at risk for SHDs, with stable external validation performance regardless of hospital type (academic/community), clinical setting (inpatient/outpatient/emergency department), or demographics (race/ethnicity). Methods: We identified 426,659 ECG-echo study pairs since 2008 from 5 NewYork-Presbyterian-affiliated (NYP) hospitals, including Columbia, to train the EchoNext DL model. ECGs were associated with SHD if acquired within one year prior to an echo reporting left ventricular (LV) ejection fraction ≤45%, LV wall thickness ≥1.3cm, moderate or severe valvular disease, moderate or large pericardial effusion, moderate or severe right ventricular dysfunction, or pulmonary artery systolic pressure ≥45 mmHg. EchoNext was tested on unseen data from four other NYP hospitals, including Cornell. Results: In the external dataset (n=21,421 pts), 33% were identified with SHD. EchoNext achieved an area under the receiver operating characteristic curve (AUROC) of 0.85, with robust results with respect to individual hospital (AUROC range: 0.82-0.87), clinical setting (0.78-0.84), and patient race/ethnicity (0.84-0.86; Figure). Conclusion: EchoNext demonstrated excellent performance in detecting SHD in the largest multi-institutional, multi-racial external validation of such a model to date. This model may allow AI-enabled reflex referral to echocardiography from ECG. Prospective trials are ongoing to validate.