Prediction of Left Ventricular Systolic Dysfunction Using an Artificial Intelligence-Based Electrocardiogram Analysis Model in Patients Presenting to the Emergency Department

Abstract

Background: Left ventricular systolic dysfunction (LVSD) is a precursor to heart failure arising from diverse cardiac conditions. Although echocardiography remains the reference standard for LVSD diagnosis, its routine use in the emergency department (ED) may be constrained by cost, time, equipment availability, and the need for specialized expertise. We evaluated the diagnostic performance of an artificial intelligence-based electrocardiogram analysis model (AI-ECG model) for detecting LVSD in patients presenting to the ED. Methods: This retrospective observational study included patients treated at a single tertiary hospital between 2020 and 2022 who underwent 12-lead electrocardiography within 24 h of ED admission and echocardiography within 30 days. Electrocardiographic data were analyzed using AiTiALVSD version 1.00.00, with a predefined cutoff score of 9.7 used to classify patients as being at high or low risk of LVSD. Diagnostic performance was assessed using standard discrimination and classification metrics. Results: Among 4529 included patients, 531 had LVSD. The AI-ECG model demonstrated high discrimination, with an area under the receiver operating characteristic curve (AUROC) of 0.934 (95% confidence interval [CI]: 0.923–0.945). Performance remained robust in patients with a shock index ≥ 0.9 (n = 453; AUROC, 0.895; 95% CI: 0.854–0.937) and in those with hypotension (n = 75; AUROC, 0.885; 95% CI: 0.786–0.984). Conclusions: The AI-ECG model accurately identified LVSD in ED patients in this cohort despite heterogeneous acquisition conditions and retained good discrimination in hemodynamically unstable subgroups, although findings in the smaller hypotensive subgroup should be interpreted as exploratory.