Background/Objectives: Postoperative atrial fibrillation (POAF) is a common complication after cardiac surgery with cardiopulmonary bypass (CPB), increasing morbidity and prolonging hospitalization. This study aimed to develop and validate an exploratory prediction model that integrates perioperative inflammatory biomarkers with clinical and surgical variables to identify patients at risk of early POAF.

Methods: A prospective exploratory cohort of 89 patients undergoing coronary artery bypass grafting (CABG; n = 36), valve surgery (n = 40), or CABG–valve surgery (n = 13) was evaluated. Clinical, surgical, and proinflammatory serum biomarkers (IL-6, IL-8, IL-10, and CRP) were recorded preoperatively (T1) and at 24 h (T2) and 48 h (T3) postoperatively. Multiple-comparison adjustments were made using the Benjamini–Hochberg false discovery rate. Predictor selection was based on bootstrap-derived stability using LASSO-penalized logistic regression, and the final model was estimated using Firth’s bias-reduced logistic regression.

Results: POAF incidence was 8.3% in CABG, in contrast to 22.5% and 30.8% in valve and CABG-valve surgeries, respectively. After multiple-comparison corrections, only IL-6 at T2 postoperatively was significantly higher in patients who subsequently developed POAF. Bootstrap-based stability selection retained T2 postoperative IL-10 and magnesium concentrations in the final model, which achieved an apparent AUC of 0.776 and a bootstrap optimism-corrected AUC of 0.728, with acceptable calibration (Brier score = 0.103), negligible multicollinearity (VIF = 1.04), and a negative predictive value of 95.5% at the optimal Youden threshold.

Conclusions: Our findings support an exploratory prediction model with moderate discrimination for POAF after cardiac surgery with CPB, providing a methodological foundation for future multicenter validation studies.