Postoperative recurrence prediction model for atrial fibrillation: a meta-analysis.

Objective: To systematically evaluate a recurrence risk prediction model for patients with Atrial Fibrillation (AF) following ablation, and to provide a reference for the model establishment and optimization.

Methods: Literature retrieval was conducted in databases including PubMed, Cochrane Library, EMbase, and Web of Science to collect studies on recurrence risk prediction models for AF patients following ablation. Study quality was assessed using Prediction Model Risk of Bias Assessment Tool, and a meta-analysis was performed using MedCalc statistical software.

Results: A total of 17 studies were included, with 4 of high risk of bias, 9 of unknown risk of bias, and 4 of low risk of bias. Across all studies, forest plots and logistic regression models were the most used prediction models. The area under the receiver operating characteristic curve (AUC) values of the prediction models ranged from 0.667 to 0.920, with a median AUC of 0.852. Through the calculation of the weighted summary of the AUC, the meta-analysis yielded a total AUC of 0.815 (0.780-0.850), indicating that the prediction models have good overall discrimination for the risk of recurrence in AF patients after ablation. After excluding studies with extreme AUC values, the adjusted AUC was 0.817 (0.786-0.849), suggesting that these extreme values did not significantly affect the overall combined results. Further subgroup analysis revealed that factors such as study design, follow-up time, sample size, and data set partitioning may significantly influence model performance and heterogeneity. Meta-analysis of predictive factors referenced in at least three studies showed that gender ( = 0.862), atrial fibrillation type ( = 0.660), and left atrial diameter ( = 0.094) were predictive factors for postoperative recurrence in atrial fibrillation patients ( < 0.05). Results of Egger's test and Begg's test did not find evidence of publication bias in the studies.

Conclusion: Current predictive models can be used as clinical decision support tools, but due to certain heterogeneity and risk of bias, they are recommended to be used cautiously in clinical practice and combined with other clinical information for comprehensive judgments.