A novel nomogram for predicting the recurrence of atrial fibrillation in patients treated with first-time radiofrequency catheter ablation for atrial fibrillation.

INTRODUCTION

The purpose of this study was to investigate the predictive factors of atrial fibrillation (AF) recurrence in patients after first-time radiofrequency catheter ablation (RFCA) and to develop a nomogram predictive model that can provide valuable information for determining the ablation strategy.

METHODS

In total, 500 patients who had received first-time RFCA for AF were retrospectively enrolled in the study. The patients were divided into a training cohort ( = 300) and a validation cohort ( = 200) randomly at a 6:4 ratio. Lasso and multivariate logistic regression analyses were used to screen the predictors for AF recurrence during a 2-year follow-up. The C-index and a calibration plot were used to detect the discriminative ability and calibration of the nomogram. The performance of the nomogram was assessed compared with the APPLE score, CAAP-AF score, and MB-LATER score using the receiver operating characteristic (ROC) curve, decision curve analysis (DCA), integrated discrimination index (IDI), and net reclassification index (NRI).

RESULTS

A total of 78 patients experienced the recurrence of AF after first-time RFCA in the training cohort. The six strongest predictors for AF recurrence in the training cohort were persistent AF, duration of AF, left atrial diameter (LAD), estimated glomerular filtration rate (eGFR), N-terminal pro-brain natriuretic peptide (NT-proBNP), and autoantibody against M2-muscarinic receptor (anti-M2-R). Based on the above six variables, a nomogram prediction model was constructed with a C-index of 0.862 (95% CI, 0.815-0.909), while the C-index was 0.831 (95% CI, 0.771-0.890) in the validation cohort. DCA showed that this nomogram had greater net benefits compared with other models. Furthermore, the nomogram showed a noticeable improvement in predictive performance, sensitivity, and reclassification for AF recurrence compared with the APPLE score, CAAP-AF score, or MB-LATER score.

CONCLUSION

We established a novel predictive tool for AF recurrence after the first-time RFCA during a 2-year follow-up period that could accurately predict individual AF recurrence.