Cost-Effectiveness of AI-Enabled Electrocardiograms for Early Detection of Low Ejection Fraction: A Secondary Analysis of the EAGLE Trial

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).

Participants & Methods

In a post-hoc analysis of the electrocardiogram (ECG) AI-Guided Screening for Low Ejection Fraction (EAGLE) trial, we developed a decision analytic model for patients 18 years and older without previously diagnosed heart failure (HF) 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 echocardiogram; 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 (QALY), 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-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 $1,651/QALY) than in inpatient or emergency room settings.

Conclusion

Implementing AI-guided targeted screening for low EF in routine clinical practice was cost-effective.

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