Abstract
Purpose: Atrial fibrillation (AF) is a common arrhythmia with increasing prevalence and significant clinical impact. Catheter ablation has emerged as a treatment option for drug-resistant AF, with variable success rates. This study aimed to develop a machine learning-based predictive model incorporating interatrial, periatrial, and epicardial adipose tissue volumes to predict AF recurrence after pulmonary vein ablation.
Methods: This retrospective cohort study included patients who underwent a first ablation procedure between 2017 and 2022. Computed tomography (CT) scans were used to measure left atrial volume (LAV), periatrial (PAT), interatrial (IAT) and (EAT) epicardial adipose tissue volumes. Two models were created and trained under three machine learning techniques. Receiver Operating Characteristic (ROC) curve analysis, accuracy, precision, recall and F1-score were evaluated. SHapley Additive exPlanations (SHAP) analysis was also conducted.
Results: From the initial 85 patients, 69 with complete follow-up and CT scan quality were included. Persistent AF, increased left atrial, PAT and IAT volumes were significantly associated with recurrence. The model including clinical and radiological variables achieved accuracies of 0.86, 0.66, and 0.86 and AUCs of 0.91, 0.87, and 0.92 in the testing group by using MLP Classifier Neural Network, Naïve Bayes, and Logistic Regression, respectively. SHAP analysis emphasized the LAV, PAT volume and AF type for recurrence prediction.
Conclusion: This study presents a machine learning explicative approach incorporating cardiac adipose tissue volumes for predicting AF post-ablation recurrence. The logistic regression model including clinical and radiological variables demonstrated the highest performance, highlighting the potential of using multimodal data for post-ablation recurrence prediction.