Opportunistic Screening for Asymptomatic Left Ventricular Dysfunction Using Electrocardiographic Artificial Intelligence: A Cost-Effective Approach.

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Abstract

The burden of asymptomatic left ventricular dysfunction (LVD) is greater than that of heart failure; however, a cost-effective tool for asymptomatic LVD screening has not been well validated. We aimed to prospectively validate an artificial intelligence (AI)-enabled electrocardiogram (ECG) algorithm for asymptomatic LVD detection and evaluate its cost-effectiveness for opportunistic screening.In this prospective observational study, patients undergoing ECG at outpatient clinics or health check-ups were enrolled in two hospitals in Taiwan. Patients were stratified into LVD (LVEF <= 40%) risk groups using a previously developed ECG algorithm. The performance of AI-ECG was used to conduct a cost-effectiveness analysis of LVD screening compared with no screening. Incremental cost-effectiveness ratio (ICER) and sensitivity analyses were employed to examine the cost-effectiveness and robustness of the results.Among the 29,137 patients, the algorithm demonstrated area-under-the-curves of 0.984 and 0.945 for detecting LVD within 28 days in the two hospital cohorts. For patients not initially scheduled for an echocardiogram, the algorithm predicted future echocardiograms (high-risk, 46.2%; medium-risk, 31.4%; low-risk, 14.6%) and LVD at 12 months (high-risk, 26.2%; medium-risk, 3.4%; low-risk, 0.1%). Opportunistic screening with AI-ECG could result in a negative ICER of -$7,439 for patients aged 65, with consistent cost-savings across age groups and particularly in men. Approximately 91.5% of the cases were found to be cost-effective at the willingness-to-pay of $30,000 in the probabilistic analysis.The use of AI-ECG for asymptomatic LVD risk stratification is promising, and opportunistic screening in outpatient clinics has the potential to save costs.Copyright © 2023. Published by Elsevier Inc.