Abstract 14057: Detection of Left Ventricular Systolic Dysfunction From Electrocardiographic Images

Abstract

Background: Left ventricular systolic dysfunction (LVSD) is associated with an over 8-fold risk of heart failure and 2-fold risk of premature death. Echocardiographic screening is limited by access to the technology, and artificial intelligence-based tools have been limited to ECG signals, precluding access by end users. Methods: A total of 393,910 12-lead ECGs from patients undergoing echocardiography at Yale were used to develop a model identifying LV ejection fraction < 40% in printed ECG images, using a EfficientNet-B3 convolutional neural network with transfer learning. The algorithm was validated using real-world ECG images. To allow interpretability, gradient-weighted class activation mapping (Grad-CAM) was used to localize class-discriminating signals in the images. Results: A total of 60,293 ECGs (15.3%) were performed in patients with LVSD. The ECG-based image identified LVSD accurate across multiple image formats in the held-out test set (AUROC 0.91, AUPRC 0.69), and in two external sets of real-world ECG images from an outpatient academic center (AUROC 0.93, AUPRC 0.69) and a rural hospital system (AUROC 0.91, AUPRC 0.89). An ECG suggestive of LVSD portended over 24-fold higher odds of LVSD in the held-out set (OR 24.4, 95% CI, 22.4-26.6). Regardless of the ECG layout, class-discriminative patterns from Grad-CAM localized to leads V2 and V3, corresponding to the left ventricle. Moreover, a positive ECG screen in individuals with LV ejection fraction > 40% at the time of initial assessment was associated with 4-fold increased risk of developing incident LV systolic dysfunction in the future (HR 4.3, 95% CI 3.9-4.7, median follow up 2.3 years). Conclusions: We developed and validated a layout-independent deep learning model that identifies LVSD from images of ECGs. This approach represents an automated and accessible screening strategy for LVSD, particularly in low resource settings.