Deep-learning algorithm for predicting left ventricular systolic dysfunction in atrial fibrillation with rapid ventricular response

초록

Aims Although evaluation of left ventricular ejection fraction (LVEF) is crucial for deciding the rate control strategy in patients with atrial fibrillation (AF), real-time assessment of LVEF is limited in outpatient settings. We aimed to investigate the performance of artificial intelligence-based algorithms in predicting left ventricular systolic dysfunction (LVSD) in patients with AF and rapid ventricular response (RVR). Methods and results This study is an external validation of a pre-existing deep-learning algorithm based on residual neural network architecture. Data were obtained from a prospective cohort of AF with RVR at a single centre between 2018 and 2023. Primary outcome was the detection of LVSD, defined as a LVEF ≤40%, assessed using 12-lead electrocardiography. Secondary outcome involved predicting LVSD using 1-lead electrocardiography (lead I). Among 423 patients, 241 with available echocardiography data within 2 months were evaluated, of whom 54 (22.4%) were confirmed to have LVSD. Deep-learning algorithm demonstrated fair performance in predicting LVSD (area under the curve [AUC] 0.78). Negative predictive value for excluding LVSD was 0.88. Deep-learning algorithm resulted competent performance in predicting LVSD compared to N-terminal prohormone of brain natriuretic peptide (AUC 0.78 vs. 0.70, p=0.12). Predictive performance of the deep-learning algorithm was lower in 1-lead electrocardiography (AUC 0.68), however, negative predictive value remained consistent (0.88). Conclusion Deep-learning algorithm demonstrated competent performance in predicting LVSD in patients with AF and RVR. In outpatient setting, use of artificial intelligence-based algorithm may facilitate prediction of LVSD and earlier choice of drug, enabling better symptom control in AF patients with RVR.

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