Machine learning models demonstrated high accuracy in predicting atrial fibrillation recurrence after catheter ablation, achieving a pooled C-index of 0.787 in validation sets.
Meta-Analysis (n=16,251)
Do machine learning models accurately predict atrial fibrillation recurrence after catheter ablation?
Machine learning models, particularly logistic regression and convolutional neural networks, demonstrate high accuracy in predicting atrial fibrillation recurrence after catheter ablation.
Effect estimate: C-index 0.787 (95% CI 0.752 to 0.821)
Background: Accurate detection of atrial fibrillation (AF) recurrence after catheter ablation is crucial. In this study, we aimed to conduct a systematic review of machine-learning-based recurrence detection in the relevant literature. Methods: We conducted a comprehensive search of PubMed, Embase, Cochrane, and Web of Science databases from 1980 to December 31, 2022 to identify studies on prediction models for AF recurrence risk after catheter ablation. We used the prediction model risk of bias assessment tool (PROBAST) to assess the risk of bias, and R4.2.0 for meta-analysis, with subgroup analysis based on model type. Results: After screening, 40 papers were eligible for synthesis. The pooled concordance index (C-index) in the training set was 0.760 (95% confidence interval CI 0.739 to 0.781), the sensitivity was 0.74 (95% CI 0.69 to 0.77), and the specificity was 0.76 (95% CI 0.72 to 0.80). The combined C-index in the validation set was 0.787 (95% CI 0.752 to 0.821), the sensitivity was 0.78 (95% CI 0.73 to 0.83), and the specificity was 0.75 (95% CI 0.65 to 0.82). The subgroup analysis revealed no significant difference in the pooled C-index between models constructed based on radiomics features and those based on clinical characteristics. However, radiomics based showed a slightly higher sensitivity (training set: 0.82 vs. 0.71, validation set: 0.83 vs. 0.73). Logistic regression, one of the most common machine learning (ML) methods, exhibited an overall pooled C-index of 0.785 and 0.804 in the training and validation sets, respectively. The Convolutional Neural Networks (CNN) models outperformed these results with an overall pooled C-index of 0.862 and 0.861. Age, radiomics features, left atrial diameter, AF type, and AF duration were identified as the key modeling variables. Conclusions: ML has demonstrated excellent performance in predicting AF recurrence after catheter ablation. Logistic regression (LR) being the most widely used ML algorithm for predicting AF recurrence, also showed high accuracy. The development of risk prediction nomograms for wide application is warranted.
Fan et al. (Thu,) conducted a meta-analysis in Atrial fibrillation recurrence after catheter ablation (n=16,251). Machine learning prediction models was evaluated on Pooled concordance index (C-index) in the validation set (C-index 0.787, 95% CI 0.752 to 0.821). Machine learning models demonstrated high accuracy in predicting atrial fibrillation recurrence after catheter ablation, achieving a pooled C-index of 0.787 in validation sets.