Risk model for predicting complications in patients undergoing atrial fibrillation ablation.

Background: Predictors of complications from atrial fibrillation (AF) ablation have been identified in small studies. The combination of risk factors to predict complications after ablation has not yet been explored.

Objective: The purpose of this study was to develop a risk score model that predicts complications after AF ablation.

Methods: The National Inpatient Sample database was used to identify 106,105 patients who underwent AF ablation. The study population was split into derivation cohort (DC; 2007-2010; n = 56,658) and validation cohort (VC; 2011-2013; n = 49,447). The multivariate predictors of any complication were identified in DC using regression analysis, and a risk score model was developed. The cohorts were divided into 5 groups (risk score in parentheses): group 0 (0), group 1 (1-10), group 2 (11-20), group 3 (21-30), and group 4 (31-61).

Results: Patients in VC were older, likely to be white, female and had a higher prevalence of comorbidities. The overall complication rate (6.9% vs 8.3%; P < .0001) and inhospital mortality rate (0.3% vs 0.5%; P < .0001) were lower in VC than in DC. A multivariate analysis yielded 9 predictors for any complication (weightage points in parentheses): cerebrovascular accident (19), congestive heart failure (12), coagulopathy (11), renal failure (7), peripheral vascular disease (6), age ≥50 years (2), female sex (2), chronic obstructive lung disease (1), and nonwhite (1). In the overall cohort, the risk of complications in groups 0, 1, 2, 3, and 4 was 3.6%, 6.5%, 15.5%, 29.5%, and 45.7%, respectively, and inhospital mortality was 0%, 0.2%, 2%, 4.6%, and 6.1%, respectively. Similar trends were observed in DC and VC.

Conclusion: A practical risk score model can be used preoperatively to risk stratify patients undergoing AF ablation.