Prediction model of atrial fibrillation recurrence after Cox-Maze IV procedure in patients with chronic valvular disease and atrial fibrillation based on machine learning algorithm.

Abstract

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia, and Cox-maze IV procedure (CMP-IV) is a commonly employed surgical technique for its treatment. Currently, the risk factors for atrial fibrillation recurrence following CMP-IV remain relatively unclear. In recent years, machine learning algorithms have demonstrated immense potential in enhancing diagnostic accuracy, predicting patient outcomes, and devising personalized treatment strategies. This study aims to evaluate the efficacy of CMP-IV on treating chronic valvular disease with AF, utilize machine learning algorithms to identify potential risk factors for AF recurrence, construct a CMP-IV postoperative AF recurrence prediction model. A total of 555 patients with AF combined with chronic valvular disease, who met the criteria, were enrolled from January 2012 to December 2019 from the Second Xiangya Hospital of Central South University and the Affiliated Xinqiao Hospital of the Army Medical University, with an average age of (57.95±7.96) years, including an AF recurrence group (n=117) and an AF non-recurrence group (n=438). Kaplan-Meier method was used to analyze the sinus rhythm maintenance rate, and 9 machine learning models were developed including random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), bootstrap aggregating, logistic regression, categorical boosting (CatBoost), support vector machine, adaptive boosting, and multi-layer perceptron. Five-fold cross-validation and model evaluation indicators [including F1 score, accuracy, precision, recall, and area under the curve (AUC)] were used to evaluate the performance of the models. The 2 best-performing models were selected for further analyze, including feature importance evaluation and Shapley additive explanations (SHAP) analysis, identifying AF recurrence risk factors, and building an AF recurrence risk prediction model. The 5-year sinus rhythm maintenance rate for the patients was 82.13% (95% CI 78.51% to 85.93%). Among the 9 machine learning models, XGBoost and CatBoost models performed best, with the AUC of 0.768 (95% CI 0.742 to 0.786) and 0.762 (95% CI 0.723 to 0.801), respectively. Feature importance and SHAP analysis showed that duration of AF, preoperative left ventricular ejection fraction, postoperative heart rhythm, preoperative neutrophil-to-lymphocyte ratio, preoperative left atrial diameter, preoperative heart rate, and preoperative white blood cell were important factors for AF recurrence. Conclusion: Machine learning algorithms can be effectively used to identify potential risk factors for AF recurrence after CMP-IV. This study successfuly constructs 2 prediction model which may enhance individualized treatment plans.