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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.

      Graphical abstract

      Keywords

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