An AI-ECG-Based Approach for Dynamic Assessment of Heart Failure Risk and Myocardial Recovery Following Atrial Fibrillation Ablation

Background Artificial intelligence-enabled electrocardiography (AI-ECG) has emerged as a promising digital biomarker for detecting latent myocardial dysfunction and predicting cardiovascular risk. However, whether serial AI-derived risk estimates reflect myocardial recovery following therapeutic intervention remains unknown.

Objective The DYNAMIC-AF HF Study aims to evaluate longitudinal changes in AI-ECG-derived heart failure (HF) risk after catheter ablation in patients with atrial fibrillation (AF) and heart failure with mildly reduced ejection fraction (HFmrEF), and to determine their association with conventional markers of reverse remodeling.

Methods The DYNAMIC-AF HF Study is a prospective multicenter observational cohort study enrolling 1,000 patients with symptomatic AF and HFmrEF undergoing first-time catheter ablation. Eligible participants must have a left ventricular ejection fraction of 41-49% and at least one predefined HF-related feature suggestive of latent myocardial dysfunction. Serial 12-lead electrocardiograms, echocardiography, biomarker assessments, and clinical follow-up will be performed at baseline and at 3, 6, and 12 months. AI-based ECG analysis will generate continuous HF-risk scores, enabling construction of longitudinal AI-derived HF risk trajectories. The primary endpoint is the change in AI-derived HF risk from baseline to 12 months. Secondary endpoints include changes in left ventricular ejection fraction, global longitudinal strain, N-terminal pro-B-type natriuretic peptide levels, AF recurrence, HF hospitalization, and mortality.

Conclusions This study will evaluate whether serial AI-ECG assessment can serve as a dynamic digital biomarker of myocardial recovery following AF ablation and support future AI-enabled monitoring and clinical decision-support strategies in cardiovascular care.