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

Objectives: 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.

Methods: 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.

Results: 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.

Keywords: Cox-maze IV procedure; atrial fibrillation; machine learning; prediction model; risk factors.

图1

窦性心律维持率曲线 Figure 1 Sinus rhythm maintenance rate curve

图2

9个机器学习模型的受试者操作特征曲线 Figure 2 Receiver operating characteristic (ROC) curves of the 9 machine learning models SVM: Support vector machine; LR: Logistic regression; RF: Random forest; XGBoost: Extreme gradient boosting; CatBoost: Categorical boosting; AdaBoost: Adaptive boosting; Bagging: Bootstrap aggregation; GBDT: Gradient boosting decision tree; MLP: Multilayer perceptron; AUC: Area under the curve.

图3

基于XGBoost和CatBoost模型的特征重要性图 Figure 3 Feature importance plots based on the XGBoost and CatBoost models A: Feature importance plot based on the XGBoost model. B: Feature importance plot based on the CatBoost model. A higher value on the X-axis indicates a higher importance of the feature in the corresponding model. AF: Atrial fibrillation; LVEF: Left ventricular ejection fraction; NLR: Neutrophil-lymphocyte ratio; LAD: Left atrial diameter; RAD: Right atrial diameter; WBC: White blood cell count; LOS: Length of hospital stay; PT: Prothrombin time; PHTN: Pulmonary hypertension; CBP: Cardiopulmonary bypass time; ALT: Alanine transaminase; TBIL: Total bilirubin; NEUT%: Percentage of neutrophils; PLT: Platelets.

图4

基于XGBoost模型(A)和CatBoost模型(B)的SHAP总结图 Figure 4 Summary plots of SHAP values based on the XGBoost (A) and CatBoost (B) models The middle part of the plot is a cluster of colored dots, where red dots represent features that have a positive impact on the model output, blue dots represent features that have a negative impact, and purple dots represent features that have a neutral impact. The size of the dots represents the magnitude of the feature’s impact, with larger dots indicating a greater impact. AF: Atrial fibrillation; LVEF: Left ventricular ejection fraction; NLR: Neutrophil-lymphocyte ratio; LAD: Left atrial diameter; RAD: Right atrial diameter; WBC: White blood cell count; RBC: Red blood cell count; LOS: Length of hospital stay; PT: Prothrombin time; CBP: Cardiopulmonary bypass time; ALT: Alanine transaminase; TBIL: Total bilirubin; NEUT%: Percentage of neutrophils.

图5

对应于特定实例的各模型预测房颤复发风险评分的SHAP实例图 Figure 5 SHAP force plots of the predicted atrial fibrillation recurrence risk scores for each model corresponding to a specific instance A and B: SHAP force plot of a low-risk example (A) and a high-risk example (B) based on the XGBoost model; C and D: SHAP force plot of a low-risk example (C) and a high-risk example (D) based on the CatBoost model. Red features and arrows indicate a positive contribution to atrial fibrillation recurrence, while blue features and arrows indicate a negative contribution. The f(x) value represents the predicted atrial fibrillation recurrence risk for a given sample, lower than the expected value indicating a decreased risk, and higher than the expected value indicating an increased risk. LEVF: Left ventricular ejection fraction; NLR: Neutrophil-lymphocyte ratio; AST: Aspartate aminotransferase; LOS: Length of hospital stay; BUN: Blood urea nitrogen; LVD: Left ventricle diameter; LAD: Left atrial diameter; TBIL: Total bilirubin.