Machine learning for the prediction of atrial fibrillation recurrence after catheter ablation: A systematic review and meta-analysis.

BACKGROUND AND

OBJECTIVE

This systematic review evaluates the current state of Machine Learning (ML) methods for predicting Atrial Fibrillation (AF) recurrence following catheter ablation. With the growing use of ML, a systematic evaluation of performance and key influencing factors such as study design, data types, and reporting is needed. The main objectives are to provide an updated overview of current achievements of ML in this field, anticipate future challenges and opportunities, and derive methodological recommendations based on the findings.

METHODS

Seven databases were systematically searched, and studies proposing ML algorithms with well-documented implementation, testing, and reporting of performance metrics underwent a qualitative synthesis and risk-of-bias assessment. A meta-analysis of 17 studies was conducted using the Area Under the receiver operating characteristic Curve (AUC) as the most commonly reported performance metric.

RESULTS

The mean overall AUC was 0.81, indicating reasonable predictive accuracy, although there was substantial inter-study heterogeneity. Meta-regression identified sample size and input data type (clinical, imaging, or electrophysiological) as significant contributors to this heterogeneity. Subgroup analysis demonstrated that models incorporating complex data modalities achieved higher predictive accuracy and lower heterogeneity compared to those relying solely on simpler clinical variables.

CONCLUSION

This review quantifies the performance of ML algorithms in predicting AF recurrence and establishes a benchmark for future research. It also highlights key challenges, including the lack of standardized datasets and limited generalizability. Incorporating more complex data sources may improve model performance, reduce inconsistencies, and enhance the potential clinical applicability of ML models in guiding patient management.