Cost-Effectiveness of Artificial Intelligence-Enabled Electrocardiograms for Early Detection of Low Ejection Fraction: A Secondary Analysis of the Electrocardiogram Artificial Intelligence-Guided Screening for Low Ejection Fraction Trial

Abstract

Objective: To investigate the cost-effectiveness of using artificial intelligence (AI) to screen for low ejection fraction (EF) in routine clinical practice using a pragmatic randomized controlled trial (RCT). Patients and Methods: In a post hoc analysis of the electrocardiogram (ECG) AI-guided screening for low ejection fraction trial, we developed a decision analytic model for patients aged 18 years and older without previously diagnosed heart failure and underwent a clinically indicated ECG between August 5, 2019, and March 31, 2020. In the previously published RCT, the intervention arm underwent an AI-guided targeted screening program for low EF with a workflow embedded into routine clinical practice—AI was applied to the ECG to identify patients at high-risk and recommended clinicians to order an ECG and the control arm received usual care without the screening program. We used results from the RCT for rates of low EF diagnosis and a lifetime Markov model to project the long-term outcomes. Quality-adjusted life years (QALYs), costs of intervention and treatment, disease event costs, incremental cost-effectiveness ratio (ICER), and cost for the number needed to screen. Multiple scenario and sensitivity analyses were performed. Results: Compared with usual care, AI-integrated ECG was cost effective, with an incremental cost-effectiveness ratio of $27,858/QALY. The program remained cost effective even with a change in patient age and follow-up time duration, although the specific ICER values varied for these parameters. The program was more cost effective in outpatient settings (ICER $1651/QALY) than in inpatient or emergency room settings. Conclusion: Implementing AI-guided targeted screening for low EF in routine clinical practice was cost effective.