Feasibility of Predicting the Risk of Atrial Fibrillation after Coronary Artery Bypass Surgery with Logistic Regression Model

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Abstract
Background and Aims: The aim of this study was to determine the risk factors of postoperative atrial fibrillation (AF) after coronary artery bypass grafting and to create predictive model and to evaluate the effects of AF on patients outcome. Material and Methods: Data of 3,676 consecutive patients were analysed to identify the predictors of AF. Multivariate logistic regression model was validated prospectively in 1,107 patients. Results: Increasing age (p < 0.001), preoperative use of digoxin (p = 003), need of intra-aortic balloon pump or inotropic medication in the weaning off cardiopulmonary by pass or during the first 24 hours postoperatively (p = 0.013), increasing body surface area (p = 0.006) and lower ejection fraction (p = 0.048) were independent risk factors for postoperative AF. The predictive model gave area under the receiver-operating characteristic (ROC) curve 0.682, 95 % confidence interval 0.663–0.701, and p < 0.001. The patients with AF incidence had more postoperative stroke (p = 0.008), confusion (p < 0.001) severe gastrointestinal complications (p = 0.005), readmission to ICU (p < 0.001), longer ICU (p < 0.001) and hospital stay (p < 0.001) when compared with the patients who remained in sinus rhythm. Conclusion: Logistic regression model with the parameters used was not accurate enough for clinical purposes. Postoperative AF is associated with postoperative stroke, severe gastrointestinal complications, readmission to ICU, and longer ICU and hospital stay.