Artificial intelligence-powered ECG is superior to NT-proBNP in predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response
O Baqal, C Yee, J Quillen, X C Mee, G K Lim, C Ayoub, R Arsanjani, D Sorajja, P Noseworthy, H El MasryAbstract
Introduction
Despite its prevalence, risk stratification for tachycardia-induced cardiomyopathy remains poorly understood. A deep learning model developed at Mayo Clinic uses standard 12-lead electrocardiograms (ECGs) to predict reduced LVEF (≤35%) in the general population with high accuracy (AUC 0.93)
Purpose
We investigated the performance of AI-ECG in predicting systolic dysfunction among patients with atrial fibrillation and rapid ventricular response, and compared it with NT-proBNP.
Methods
We conducted a retrospective study (IRB approval #25-000904) of adult patients (age ≥ 18 years) evaluated across our Clinic enterprise with atrial fibrillation and rapid ventricular response (heart rate >100 bpm), including AF occurring in-hospital and AF at presentation, between January 2017 and May 2025. We excluded patients with a known history of heart failure with reduced EF (<50%). We also collected data on NT-proBNP levels when available within one week of the ECG and echocardiogram.
Results
A total of 11,801 patients were included, with a mean age of 73.2 +- 12.6 years, 48.6% female and 94.8% White. A combination of AI ECG low EF probability cutoff of 75% had specificity of 96% and 98% NPV in ruling out LVEF < 35%. A combination of AI ECG low EF probability cutoff of 17% and LVEF <35% provided the best balance of sensitivity and specificity, 66% and 78%, respectively with an accuracy of 78%. NT-proBNP data was available on 5,279 patients (48%). Mean NT-proBNP level was 4090 pg/mL. Based on ROC curve analysis, AI ECG was superior to NT-proBNP in predicting LVEF <35% (AUC 0.72 vs 0.59), with accuracy of 77% versus 47% at Youden’s cutpoints of AI-ECG probability of 17% and NT-proBNP level of 1767 pg/mL, respectively
Conclusions
Our study demonstrates the utility of AI-ECG in predicting reduced LVEF among patients with AF RVR. AI-ECG demonstrated excellent specificity and NPV in ruling out reduced LVEF, serving as a powerful clinical screening tool to assist with the pharmacologic choice of rate control and could change the timing of further echocardiographic evaluation. Our study offers novel insights paving the way for future investigations to help fine-tune and further delineate the role of AI ECG in risk stratifying patients with suspected tachycardia-induced cardiomyopathy, guiding therapeutic and diagnostic clinical decisions