Accurately predicting AF recurrence after catheter ablation remains challenging, and current tools often fail to capture the full complexity of arrhythmia substrate. This retrospective study, published in Clinical Cardiology, developed a model that integrates electrophysiological substrate metrics, structural remodeling markers, and inflammatory–metabolic biomarkers to improve post-ablation risk prediction.

The analysis included 279 patients undergoing first-time AF ablation between June 2022 and January 2024, with 12 months of follow-up. Four independent predictors emerged from multivariate logistic regression: LVZ extent, hs-CRP, RDW, and LAD. These parameters reflect key domains of AF pathophysiology, including atrial fibrosis, inflammation, hematologic variability, and chamber enlargement.

The composite model achieved an AUC of 0.84 in the validation set, providing stronger discrimination than the APPLE score (AUC 0.73, p < 0.001) and outperforming LAD (0.77) and LVZ (0.75) when used alone. It also stratified recurrence risk into five clinically meaningful categories, ranging from less than 5% to greater than 70%.

These findings indicate that an integrated substrate-biomarker approach can enhance post-ablation risk assessment and may support more individualized management pathways in atrial fibrillation.