Development and Validation of a Postoperative Prognostic Nomogram to Predict Recurrence in Patients with Persistent Atrial Fibrillation: A Retrospective Cohort Study

Background: Patients with persistent atrial fibrillation (PsAF) have a high risk of recurrence after catheter radiofrequency ablation. Nevertheless, no effective prognostic tools have been developed to identify these high-risk patients to date. This study sought to develop and validate a simple linear predictive model for predicting postoperative recurrence in patients with PsAF. Methods: From June 2013 to June 2021, patients with PsAF admitted to our hospital were enrolled in this single-center, retrospective, observational study. The characteristics substantially associated with recurrence in patients with PsAF were screened through univariate and multivariate logistic regression analysis. The receiver operating characteristic curve was used to assess the predictive significance of the nomogram model after nomogram development. Furthermore, to assess the clinical value of the nomogram, we performed calibration curve and decision curve analyses. Results: A total of 209 patients were included in the study, 42 (20.10%) of whom were monitored up to 1 year for recurrent AF. The duration of AF episodes, left atrial diameter, BMI, CKMB, and alcohol consumption were found to be independent risk factors (P<0.05) and were integrated into the nomogram model development. The area under the curve was 0.895, the sensitivity was 93.3%, and the specificity was 71.4%, thus indicating the model’s excellent predictive ability. The C-index of the predictive nomogram model was 0.906. Calibration curve and decision curve analyses further revealed that the model had robust prediction and strong discrimination ability. Conclusion: This simple, practical, and innovative nomogram can help clinicians in evaluation of the risk of PsAF recurrence after catheter ablation, thus facilitating preoperative evaluation, postoperative monitoring and ultimately the construction of more personalized therapeutic protocols.