Background and Objectives: Postoperative delirium (POD) is a frequent and severe complication following cardiac surgery, particularly in high-risk patients undergoing coronary artery bypass grafting (CABG) and aortic valve replacement (AVR). Despite extensive research, predicting POD remains challenging due to the multifactorial and often non-linear nature of its risk factors. This study aimed to improve POD prediction using an interpretable machine learning approach and to explore the combined effects of clinical, biochemical, and perioperative variables. Materials and Methods: This study included 131 patients who underwent CABG or AVR. POD occurrence was assessed using standard diagnostic criteria. Clinical, biochemical, and perioperative variables were collected, including patient age, sedation type, and mechanical ventilation status. Machine learning analysis was performed using an XGBoost classifier, with model interpretation achieved through SHapley Additive exPlanations (SHAP). Univariate logistic regression was applied to identify significant predictors, while SHAP analysis revealed variable interactions. Results: POD occurred in 34.3% of patients (n = 45). Patients who developed POD were significantly older (67.7 ± 6.5 vs. 64.5 ± 8.7 years, p = 0.020). Sedation with mechanical ventilation and the type of sedative used were strongly associated with POD (both p < 0.001). Sedation during mechanical ventilation showed the strongest association (OR = 2520.0; 95% CI: 80.9–78,506.7; p < 0.00001). XGBoost classifier achieved excellent performance (AUC = 0.998, accuracy = 97.6%, F1 score = 0.976). SHAP analysis identified sedation, mechanical ventilation, and their interactions with fibrinogen, troponin I, leukocyte parameters, and lung infection as key predictors. Conclusions: This study demonstrates that an interpretable machine learning approach can enhance POD prediction, providing insights into the combined impact of multiple clinical, biochemical, and perioperative factors. Integration of such models into perioperative workflows may enable early identification of high-risk patients and support individualized preventive strategies.

Breast cancer, a prevalent global malignancy in women, necessitates a com-prehensive treatment approach, with surgery playing a crucial role. Severe acute pain is common post-radical breast cancer surgery, emphasizing the significance of hemodynamic stability and postoperative pain control for optimal outcomes. This study evaluates the impact of ultra-sound-guided erector spinae plane block (ESPB) on these parameters in ASA scores 1-2 patients undergoing modified radical breast cancer surgery with general anesthesia. Methods: Forty-eight patients were divided into two groups: general anesthesia with erector spinae plane block (GA+ESPB) and a control group receiving only general anesthesia (GA). Hemodynamic parameters were continuously monitored, and postoperative pain was assessed using the visual analog scale (VAS) at various time points. Results: Ultrasound-guided ESPB effectively maintained hemody-namic stability and reduced postoperative pain in breast cancer surgery patients. Statistically significant differences were observed in heart rate, systolic and diastolic blood pressure, and mean arterial pressure between the GA and GA+ESPB groups at multiple time points (p<0.05). VAS scores showed a significant interaction time*group (p<0.001), with consistent differences between the groups at all time points (p≤0.001). Conclusion: Ultrasound-guided ESPB application proved ef-fective in preserving hemodynamic stability and managing postoperative pain in modified radical breast cancer surgery. The technique demonstrates promise in minimizing complications related to hemodynamic variations and postoperative pain, contributing to a comprehensive approach in breast cancer surgical treatment.