Introduction: Postoperative atrial fibrillation (POAF) is the most common complication after cardiac sur-gery, with increased risk of stroke and higher mortality. Objective: Our aim was to identify the patient 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 repla-cement (SAVR) between January 2000 and June 2014. The patients were followed during the early postopera-tive period, until their discharge.
Results: Atrial fibrillation (AF) occurred in 342 pati-ents (28.71%). Six variables associated with higher ar-rhythmic risk (advanced age, body mass index, tricus-pid regurgitation, prolonged ventilation, longer staying in the intensive care units and dilated left atrium (≥35 ml/m²)) were selected to form a multivariate predicti-on model that predicts POAF in 64.7% cases. This pre-diction model had a moderate discriminative power (AUC=0.65). We also developed a CHAID model showing multilevel interactions among risk factors for POAF, age being the variable with the greatest discri-minative power, patients older than 68 years being at higher risk. In patients with low-risk, the subgroup with dilated left atrium has more chances to develop POAF. For intermediate risk group, the history of AF is the next in decision tree. In the high-risk group, the tricuspid regurgitation at least moderate was the next predictive variable.
Conclusions: CHAID derived model is a tool, that co-uld be easily applied to identify patients at risk of pa-roxysmal atrial fibrillation after isolated surgical aortic valve replacement. This model predicts postoperative atrial fibrillation in two third of patients, with a mode-rate discriminative power.