Establishment and Validation of the Nomogram Model and the Probability of Silent Cerebral Infarction After Ablation Atrial Fibrillation.

ABSTRACT

BACKGROUND: The objective of this study is to establish and validate a nomogram model for predicting the probability of silent cerebral infarction following ablation of atrial fibrillation. METHODS AND RESULTS: A retrospective observational study was conducted on the data of 238 patients with atrial fibrillation who underwent radiofrequency ablation in our hospital from October 2019 to December 2022. LASSO regression and multivariate logistics regression analysis were used to assess the independent risk factors for silent cerebral infarction after ablation. The AUC of the predictive model was 0.733 (95% CI, 0.649-0.816) and the internal validation (bootstrap = 1000) of the bootstrap method was 0.733 (95% CI 0.646-0.813). The Hosmer-Lemeshow test yields an insignificant p-value of X-squared = 10.212 and p-value = 0.2504, thus indicating an insignificant difference between predicted and observed values and good calibration results. The clinical impact curve (CIC) and clinical decision curve also prove that this graph is useful in the clinical setting. CONCLUSION: We developed an easy-to-use nomogram model to predict the probability of silent cerebral infarction following radiofrequency ablation of atrial fibrillation. This model can provide a valid assessment of the probability of postoperative silent cerebral infarction in patients undergoing radiofrequency ablation of atrial fibrillation.