Key result
XGBoost model integrating ablation quality metrics predicts 1-year AF recurrence with ~0.92 AUC.
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Why the study?
Current prediction models for AF recurrence after ablation rely mainly on nonmodifiable clinical characteristics and have limited predictive accuracy, offering little guidance for intraprocedural decision-making.
Does integrating ablation quality metrics with clinical characteristics in a machine learning model improve the prediction of 1-year AF recurrence in patients undergoing first AF ablation?
Cohort (n=1,782)
Yes
Does integrating ablation quality metrics with clinical characteristics in a machine learning model improve the prediction of 1-year AF recurrence in patients undergoing first AF ablation?
Effect estimate: AUC 0.918 (95% CI 0.874-0.962)
Integrating intraprocedural ablation quality metrics with clinical data in a machine learning model substantially improves the prediction of 1-year atrial fibrillation recurrence after first-time ablation.
May improve post-ablation AF recurrence risk stratification; extends prior models but remains hypothesis-generating pending validation.
BACKGROUND: Current prediction models for atrial fibrillation (AF) recurrence after ablation rely predominantly on nonmodifiable clinical characteristics, demonstrating limited predictive accuracy and offering little guidance for intraprocedural decision-making and outcome optimization. OBJECTIVES: This study aimed to develop and externally validate a machine learning model that integrated clinical characteristics with ablation quality metrics to individually predict AF recurrence. METHODS: We conducted a multicenter, retrospective cohort study of patients undergoing their first AF ablation in 14 hospitals from mainland China. Patients aged 18 to 80 years were enrolled, and data on clinical characteristics and quality metrics were collected. An XGBoost model was developed using 7 selected features to predict 1-year AF recurrence. The model was developed using 5-fold cross-validation for internal validation and was further externally validated on data from 2 independent hospitals. RESULTS: Among 1,782 patients enrolled between December 2021 and December 2023, 294 (16.5%) experienced AF recurrence within 1 year. The final XGBoost model achieved strong discrimination in internal validation (area under the curve [AUC]: 0.918, 95% CI: 0.874-0.962). In feature-combination analyses, model performance improved from an AUC of 0.588 using clinical features alone to 0.918 after incorporating ablation quality metrics. In 2 independent external validation centers (n = 239), the model showed good discrimination, with AUCs of 0.992 and 0.951 and precision-recall AUCs of 0.960 and 0.815, respectively. CONCLUSIONS: Integrating ablation quality metrics yielded an validated model that showed strong discrimination for 1-year AF recurrence, outperforming clinical features alone. This approach highlights the prognostic value of intraprocedural quality assessment and may support personalized patient management.
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Wang et al. (2026) conducted a cohort in Atrial fibrillation (n=1,782). Machine learning model integrating ablation quality metrics and clinical characteristics vs. Clinical features alone was evaluated on 1-year atrial fibrillation recurrence (model discrimination) (AUC 0.918, 95% CI 0.874-0.962). An XGBoost model integrating ablation quality metrics with clinical features strongly discriminated 1-year atrial fibrillation recurrence (AUC 0.918; 95% CI 0.874-0.962).
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