ArticleBMC cardiovascular disorders2026
Development and validation of a LightGBM-based model with risk stratification for predicting early recurrence after catheter ablation for atrial fibrillation.
Article in BMC cardiovascular disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAtrial fibrillation (AF) is the most prevalent sustained arrhythmia, yet tools for predicting early recurrence (ER) after catheter ablation remain limited. This study aimed to develop a machine learning model to estimate ER risk following first-time AF ablation.
methodsIn this retrospective single-center study, 519 patients undergoing initial AF ablation were enrolled (ER rate: 9.2%). Eight predictors were selected via recursive feature elimination. A LightGBM model was constructed and internally validated against logistic regression and conventional risk scores.
resultsThe LightGBM model achieved an AUC of 0.715 in training and 0.704 in testing, showing higher discrimination than logistic regression (AUC = 0.623) and traditional scores (APPLE AUC = 0.560). SHAP analysis identified mitral regurgitation severity, age, hemoglobin, and albumin as predominant predictors. Using a Youden-derived threshold (0.099), high- and low-risk groups exhibited significantly different recurrence rates in testing(11.4% vs. 2.9%; P < 0.05).
conclusionWe developed a LightGBM-based model integrating structural and metabolic features that modestly improves upon conventional approaches in predicting ER after AF ablation. This tool may facilitate personalized post-procedural management. Multicenter prospective validation is warranted.
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