Abstract 18713: The Impact of ECG-AI Design Choices on Clinical Utility: A Deep Dive Into Left Ventricular Hypertrophy, Hypertrophic Cardiomyopathy, and Cardiac Amyloidosis

Abstract

Introduction: Artificial intelligence applied to electrocardiograms (ECG-AI) enables early and efficient detection of cardiovascular disease. The technical and clinical impact of different approaches to designing ECG-AI remains under-explored. We consider left ventricular hypertrophy (LVH) and examine the impact of disease-specific labeling of minority etiologies of left ventricular wall thickening- hypertrophic cardiomyopathy (HCM) and cardiac amyloidosis (CA) - on ECG-AI performance. Hypothesis: ECG-AI designed to detect HCM or CA outperforms ECG-AI broadly trained to identify LVH due to differences in attention to key ECG features associated with HCM or CA. Methods: Independent neural networks were trained to identify HCM, CA, and LVH. HCM and CA patients were identified using the full depth of EHR data and validated via clinician adjudication (N = 3,025 and 4,251 patients, respectively). Moderate and severe LVH patients (N = 98,144) were identified using echocardiogram results and international LVH criteria. We compared the ability of disease-specific ECG-AI vs. LVH ECG-AI to detect HCM (or CA) by examining model performance within each of the three test sets. Results: 52.6% of HCM patients and 49.5% of CA patients met LVH criteria. Within the HCM test set, the AUROC, sensitivity, specificity, and positive predictive value (PPV) of the HCM ECG-AI were 0.94, 0.88, 0.85, and 60%, respectively, whereas the same for LVH ECG-AI were 0.83, 0.83, 0.64, and 37%. Within the CA test set, CA ECG-AI achieved AUROC, sensitivity, specificity, and PPV of 0.94, 0.81, 0.91, and 69%, respectively, whereas the same for LVH ECG-AI were 0.75, 0.73, 0.68, and 31%. ECGs positive for HCM exhibited higher P wave area, QRS deflection and R wave amplitude compared to LVH-positive ECGs (Cohen's D > 0.75). ECGs positive for CA exhibited lower R wave amplitude/area, QRS deflection and QRS areas (Cohen's D > 0.6). Conclusion: Models trained using carefully labeled HCM or CA identify distinct signals associated with the diseases, resulting in improved disease discrimination compared to a model trained to identify echocardiographic left ventricular thickening. Labeling choices should be communicated transparently to clinicians to ensure safe and effective use of ECG-AI.