A combined prediction model integrating left atrial diameter, NT-proBNP, and uric acid provides a significantly better and easily accessible tool for predicting atrial fibrillation recurrence after first-time radiofrequency ablation compared to individual markers.
Radiofrequency ablation is a leading clinical method for restoring sinus rhythm in atrial fibrillation (AF) patients. However, the high recurrence rate and potential complications necessitate careful evaluation of its application. This study aims to identify predictive factors for post-ablation AF recurrence by integrating multiple preoperative clinical variables in AF patients. A total of 270 patients with non-valvular AF undergoing first-time radiofrequency ablation were categorized into a recurrence group and a non-recurrence group. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for AF recurrence. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the predictive value of related factors and a combined prediction model for AF recurrence. At one-year follow-up, AF recurred in 100 patients (37.04%). Logistic multifactor regression analysis identified left atrial diameter (LAD), N-terminal pro-brain natriuretic peptide (NT-proBNP), uric acid (UA), left atrial appendage blood flow velocity (LAAV), and early recurrence of AF (ERAF) as independent predictors of AF recurrence. The AUCs of LAD, NT-proBNP, UA and the combined prediction model for predicting AF recurrence after radiofrequency ablation were 0.674, 0.685, 0.652 and 0.785, respectively, with a statistically significant difference between single indicators and the combined model (P < 0.05). LAD, NT-proBNP, UA, LAAV, and ERAF are independent predictors of atrial fibrillation (AF) recurrence. The combined model of LAD, NT-proBNP, and UA shows better predictive ability than individual factors, offering a more reliable recurrence assessment. Our model, based on these three widely accessible markers, is simple, practical, and easily generalizable. However, the lack of external validation limits its applicability. Future studies should validate the model in independent cohorts to confirm its robustness and generalizability.
Bai et al. (2026) studied this question.