Influencing factors and predictive models of recurrence after cryoballoon ablation for atrial fibrillation

Background : To construct and verify the influencing factors and early warning model of recurrence after cryoballoon ablation of atrial fibrillation. Methods: This study retrospectively enrolled 113 hospitalized patients with persistent atrial fibrillation (PAF) from the NHANES cohort between August 2019 and August 2022 as the modeling group for developing and validating the model. Additionally, 51 hospitalized PAF patients were recruited between August 2022 and August 2023 as an external validation group to assess the model's generalizability. Statistically analyze the recurrence of atrial fibrillation in patients after cryoballoon ablation. Univariate analysis and LASSO regression were used to screen for influencing factors. Logistic regression was used to analyze the factors influencing the recurrence of atrial fibrillation after cryoballoon ablation. A model was established and a nomogram was drawn. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve and decision curve (DCA) were used to evaluate the performance and applicability of the model. The incidences of MODS in the modeling group and the validation group were 27.43% (31/113) and 29.41% (15/51), respectively. Result The postoperative recurrence rates of the modeling group and the validation group were 27.43% (31/113) and 29.41% (15/51), respectively, respectively. The results of univariate analysis in the modeling group showed that the postoperative recurrence group and the postoperative non-recurrence group had diabetes, N-terminal pro-brain natriuretic peptide (NT-proBNP), left coronary artery (LAD), and left ventricular end-diastolic diameter. There were statistically significant differences in the comparisons of (LVDd), neutrophil-to-lymphocyte ratio (NLR), left ventricular ejection fraction (LVEF), B-type natriuretic peptide (BNP), and RIPV60s(all P <0.05). LASSO regression analysis screened out the variables: NT-proBNP, NLR, and RIPV60s. The results of Logistic regression analysis showed that NLR ( OR =4.987, 95%CI: 1.547-15.906), NT-proBNP ( OR =4.526, 95%CI: 1.723-11.485), and RIPV60s ( OR =3.419, 95%CI: 1.268-9.647 is a risk factor for recurrence after cryoballoon ablation of atrial fibrillation ( P <0.05). The correction curves of the modeling group and the verification group tend to approach the standard curve. The ROC curve results of the modeling group and the validation group showed that the AUC of the nomogram model for predicting the recurrence of atrial fibrillation after cryotherapy balloon ablation was 0.846 (95%CI: 0.768-0.924, P <0.001) and 0.802 (95%CI: 0.715-0.889, P <0.001), respectively. The DCA curve indicates that the modeling group has a greater net clinical benefit for patients within the threshold range of 0.05 to 0.89, while the validation group can achieve a greater net clinical benefit for patients within the threshold range of 0.04 to 0.91. Conclusion: NT-proBNP, NLR, and RIPV60s are closely related to the recurrence of atrial fibrillation after cryotherapy balloon ablation. The early warning model constructed based on this has good predictive value.