Background We previously developed and validated an AI-based ECG analysis tool (ECG Buddy) in a Korean population. This study aims to validate its performance in a U.S. population, specifically assessing its LV Dysfunction Score and LVEF-ECG feature for predicting LVEF <40%, using NT-ProBNP as a comparator.

Methods We identified emergency department (ED) visits from the MIMIC-IV dataset with information on LVEF <40% or ≥40%, along with matched 12-lead ECG data recorded within 48 hours of the ED visit. The performance of ECG Buddy’s LV Dysfunction Score and LVEF-ECG feature was compared with NT-ProBNP using Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) analysis.

Results A total of 22,599 ED visits were analyzed. The LV Dysfunction Score had an AUC of 0.905 (95% CI: 0.899 - 0.910), with a sensitivity of 85.4% and specificity of 80.8%. The LVEF-ECG feature had an AUC of 0.908 (95% CI: 0.902 - 0.913), sensitivity 83.5%, and specificity 83.0%. NT-ProBNP had an AUC of 0.740 (95% CI: 0.727 - 0.752), with a sensitivity of 74.8% and specificity of 62.0%. The ECG-based predictors demonstrated superior diagnostic performance compared to NT-ProBNP (all p<0.001).

In the Sinus Rhythm subgroup, the LV Dysfunction Score achieved an AUC of 0.913, and LVEF-ECG had an AUC of 0.917, both outperforming NT-ProBNP (0.748, 95% CI: 0.732 - 0.763, all p<0.001).

Conclusion ECG Buddy demonstrated superior accuracy compared to NT-ProBNP in predicting LV systolic dysfunction, validating its utility in a U.S. ED population.

Joonghee Kim, MD, PhD developed the algorithm. He also founded a start-up company, ARPI Inc., where he serves as the CEO. Youngjin Cho, MD, PhD works for the company as a research director. Otherwise, there is no conflict of interest for the other authors.

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number : RS-2023-00265933), and the SNUBH Research Fund (grant number: 14-2023-0335).

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

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The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2023). MIMIC-IV (version 2.2). PhysioNet. https://doi.org/10.13026/6mm1-ek67.

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