There has been a significant increase in the prevalence of atrial fibrillation (AF) over the past 30 years. Pulmonary vein isolation (PVI) is an effective treatment for AF, but research investigations have shown that AF recurrence still occurs in a significant number of patients after ablation. Heart rhythm outcomes following catheter ablation are correlated with numerous clinical factors, and researchers developed predictive models by integrating risk factors to predict the risk of recurrence of atrial fibrillation. The purpose of this article is to outline the risk scores for predicting cardiac rhythm outcomes after PVI and to discuss the modifiable factors that increase the risk of recurrence of AF, with the hope of further improving catheter ablation efficacy through preoperative identification of high-risk populations and postoperative management of modifiable risk factors.
Keywords: Atrial fibrillation; Catheter ablation; Machine learning; Prediction model; Risk factor.
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