A Random Forest machine learning model accurately predicted atrial fibrillation recurrence after catheter ablation, achieving an AUC of 0.925 in the validation set and outperforming other algorithms.
Observational (n=438)
No
Can machine learning algorithms accurately predict atrial fibrillation recurrence after catheter ablation?
A Random Forest machine learning model accurately predicts atrial fibrillation recurrence after catheter ablation, outperforming conventional risk scores.
Effect estimate: AUC 0.925
Abstract Background Atrial fibrillation (AF) is the most common arrhythmia worldwide, with catheter ablation being an effective yet recurrence-prone treatment. Given the limited accuracy of conventional risk scores in identifying patients at high risk of recurrence after catheter ablation, this study sought to develop and validate a machine learning (ML) model for predicting AF recurrence using a wide array of clinical and laboratory variables. Methods Of the 438 patients with AF included in this study who underwent catheter ablation between 2016 and 2023. Comprehensive demographic, clinical, echocardiographic, laboratory, medication, and risk score data were collected. The primary endpoint was AF recurrence, defined as documented AF, atrial flutter, or atrial tachycardia ≥ 30 s occurring ≥ 3 months post-procedure. The dataset was randomly divided into training set and validation set in a 6:4 ratio. Univariate and multivariate logistic regression were used to identify independent risk factors for the risk of recurrence after catheter ablation of AF. Eleven ML algorithms were established on the training set—including random forest (RF), gradient boosting machine(GBM), logistic regression (LR), support vector machine(SVM) and XGBoost. Model performance was evaluated using receiver operating characteristic (ROC) curves, precision-recall (PR) curves, and calculating the area under the curve (AUC). A calibration curve assessed the model’s accuracy, while decision curve analysis (DCA) evaluated its clinical applicability. In addition, to avoid overfitting, we conducted an internal validation of best model using Bootstrap. Finally, Shapley additive explanations (SHAP) were employed to interpret the importance of predictor variables. Results Of the 438 patients with AF included in this study who underwent catheter ablation, 147 experienced recurrence during follow-up. The median age of the total population was 63 years, with 64 years in the non-recurrence group and 63 years in the recurrence group ( P = 0.303). The proportion of females was 36.1% in the recurrence group vs. 52.6% in the non-recurrence group ( P = 0.018). The RF model demonstrated superior performance, achieving an AUC of 0.878 in the training set and 0.925 in the validation set. It also showed excellent calibration (Brier score: 0.186) and clinical utility across a wide risk threshold range. Key predictors included alcohol consumption OR = 2.12 (1.15–3.91), P = 0.017), fibrin degradation products FDP, OR = 1.22 (1.02–1.46), P = 0.027, and hypertension OR = 0.47 (0.26–0.85), P = 0.012. Conclusion An interpretable ML model based on RF accurately predicts AF recurrence post-ablation and outperforms conventional risk scores. This tool may enhance individualized patient counseling, follow-up strategy design, and resource allocation in clinical practice.
Wang et al. (2026) conducted an observational in Atrial fibrillation recurrence (n=438). Random Forest machine learning model vs. Other machine learning algorithms and conventional risk scores was evaluated on Prediction of AF recurrence (AUC) (AUC 0.925). A Random Forest machine learning model accurately predicted atrial fibrillation recurrence after catheter ablation, achieving an AUC of 0.925 in the validation set and outperforming other algorithms.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: