Clinical predictors of long-term atrial fibrillation recurrence post catheter ablation: an ITHACA-database analysis

Shared decision making regarding how to best manage atrial fibrillation (AF) includes the use and efficacy of catheter ablation (CA) as a treatment option. However, the long-term success rate for the procedure based on patients' baseline demographics and comorbidities remains unclear. The authors aimed to create a user-friendly predictive model to help provide an individualized risk score for long-term AF recurrence.

Ablation outcomes reported in the electronic medical records were documented from 3, 12, and 24 months, and last follow-up visit, which was an average of 42 months. Multivariate logistic regression was used to identify independent variables associated with AF recurrence. Based on respective coefficients, associated variables were used to create the ORACLE-AF (symptOmatic AF, race [White], AF type [persistent], cardioversion, late age > 70, early AF recurrence, asthma/COPD, heart failure) model. A k-1 machine learning model was used for validation, using an 80/20 development test cohort ratio.

A total of 3440 patients (69.4 ± 10.2 years, 44% women) who received a de novo CA were included in the analysis. Forty-eight percent of patients had the primary composite outcome of AF recurrence by the end of the 42-month observation period. After analysis, the ORACLE-AF model was created with an area under the curve of 0.80.

Using machine learning, the authors created a predictive tool to allow for individualized risk prediction for long-term AF recurrence following CA. These findings highlight the need to consider risk factors not typically associated with long-term AF recurrence during the pre-ablation consultation.