A Nomogram utilizing ECG P-wave parameters to predict recurrence risk following catheter ablation in paroxysmal atrial fibrillation.

OBJECTIVE

The objective of this study is to assess the predictive utility of perioperative P-wave parameters in patients with paroxysmal atrial fibrillation (PAF) undergoing catheter ablation, and to develop a predictive model using these parameters.

METHODS

A total of 213 patients with PAF undergoing catheter ablation were retrospectively analyzed. P-wave parameters were measured within 3 days preoperatively and on the day postoperatively to determine their predictive significance for postoperative PAF recurrence.

RESULTS

Post-ablation, PAF did not recur in 168 patients, while 45 experienced recurrence. Significant differences were observed in preoperative P-wave parameters as Maximum P Wave Duration(Pmax), absolute value of P Wave Terminal Force of V1 (PtfV1) and P Wave Dispersion(Pd), postoperative P-wave parameters as P Wave Duration (PWD), Pmax, P Wave Area(P-area), absolute value of PtfV1 and Pd, and changes in perioperative P-wave parameters (Delta-Pmax, Delta-PtfV1 absolute value, Delta-Pd, Delta-PWD). Univariate logistic regression, receiver operating characteristic (ROC) curve analysis, and hazard ratio assessment identified predictive indicators for postoperative recurrence, including Pmax, PtfV1 absolute value, Pd, post-P area, post-PWD and Delta-pwd). A personalized nomogram model based on these P-wave parameters was developed. Calibration curve assessment demonstrated that the predictive performance of the nomogram for PAF recurrence following catheter ablation closely matched actual observed outcomes. ROC curve analysis indicated a sensitivity of 89.3% for the model, and decision curve analysis confirmed its significantly favorable predictive use and clinical benefits.

CONCLUSIONS

P-wave parameters like PWD, PWDaVF, Pmax, Pd, and PtfV1 serve as predictors of PAF recurrence following catheter ablation. The nomogram model constructed using these P-wave parameters demonstrates robust predictive performance.