Objective: We previously developed and validated an artificial intelligence-based electrocardiogram (ECG) analysis tool (ECG Buddy) in a Korean population. This study investigated the performance of this tool in a US population, specifically assessing the left ventricular (LV) dysfunction score and LV ejection fraction (LVEF)-ECG feature for predicting LVEF <40%. The study used N-terminal pro-B-type natriuretic peptide (NT-ProBNP) as a comparator.
Methods: We identified emergency department (ED) visits from the MIMIC-IV dataset with information on LVEF <40% or ≥40% and matched 12-lead ECG data recorded within 48 hours of the ED visit. The performance of ECG Buddy's LV dysfunction score and the LVEF-ECG feature was compared with those of NT-ProBNP using area under the receiver operating characteristic curve (AUC) analysis.
Results: A total of 22,599 ED visits was analyzed. The LV dysfunction score had an AUC of 0.905 (95% confidence interval [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 of 83.5%, and specificity of 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 (AUC, 0.748; 95% CI, 0.732-0.763; all P<0.001).
Conclusion: ECG Buddy demonstrated superior accuracy compared with NT-ProBNP in predicting LV systolic dysfunction, validating its utility in a US ED population.
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