A high-risk perioperative atrial fibrillation phenotype and early outcomes after isolated coronary artery bypass grafting

Introduction Perioperative atrial fibrillation (POAF) remains the most common arrhythmic complication occurring in the setting of isolated coronary artery bypass grafting (CABG), and it is now being increasingly recognised as a marker of early postoperative morbidity, rather than a simple rhythm disturbance. The aim of the research to find the independent clinical and procedural predictors of POAF in patients with isolated CABG, as well as to assess the relationship with early clinically significant outcomes. Material and methods In this single-centre retrospective cohort analysis, consecutive adult patients who received isolated CABG surgery between January 2022 and December 2025 were included. Patients with a history of atrial fibrillation/flutter, simultaneous cardiac surgery, and incomplete perioperative records were excluded. POAF was identified as new atrial fibrillation/flutter occurring within seven days of surgery or before discharge. Multivariate logistic regression analysis was used to evaluate independent predictors of POAF. Early postoperative outcomes were compared between patients with and without POAF. Results In the study involving 550 patients, POAF occurred in 150 (27.3%) patients. Multivariate analysis showed that advanced age, chronic kidney disease, increased left atrial dimension, decreased left ventricular ejection fraction, prolonged cardiopulmonary bypass time, and perioperative red blood cell transfusion were significant predictors of POAF. Patients with POAF had increased rates of acute kidney injury (22.0% vs. 11.0%, p = 0.001), increased intensive care unit stay, and increased hospital stay, compared to those without POAF. There was no significant difference in in-hospital mortality. Conclusions In the case of isolated CABG, the incidence of POAF is related to a higher incidence of early postoperative morbidity, but it is not related to in-hospital mortality. The identification of at-risk patients with the use of clinical data could contribute to the evaluation of risk stratification.