Objective: Postoperative atrial fibrillation (POAF) is the most common complication following cardiac surgery, with increased risk of stroke and high mortality. Our aim was to identify patients at risk and to design a model that could predict POAF.
Methods: In this single center study, we evaluated 1191 patients requiring isolated surgical aortic valve replacement between January 2000 and June 2014. The patients were followed during the early postoperative period until discharge.
Results: AF occurred in 342 patients (28.71%). Six variables associated with high arrhythmic risk [advanced age, body mass index, tricuspid regurgitation, prolonged ventilation, longer intensive care unit stay, and dilated left atrium (LA; volume ≥35 ml/m2)] were selected to create a multivariate prediction model. This model predicted POAF in 64.7% of cases, with a moderate discriminative power (AUC=0.65; p=0.001; 95% CI, 0.571-0.771). We also developed the CHAID (Chi-square automatic interaction detection) model showing multilevel interactions among risk factors for POAF. Age had the greatest discriminative power, with patients aged >68 years at a higher risk for POAF. In low-risk patients, the subgroup with dilated LA (volume ≥40 ml) was more prone to develop POAF. For the intermediate-risk group, history of AF was the next deciding variable, whereas for the high-risk group, it was tricuspid regurgitation (at least moderate).
Conclusion: The multivariate logistic model has an acceptable predictive value. CHAID-derived model is a new tool that could be easily applied to identify patients requiring prophylactic regimens.