Beyond the abdomen: an interpretable machine learning model for predicting postoperative ileus in non-abdominal surgery.

Purpose: Postoperative ileus (POI) following non-abdominal surgery is an underestimated complication. This study aimed to develop and validate a machine learning model to predict POI risk, specifically integrating brain-gut axis variables, including depression history and chronic selective serotonin reuptake inhibitor (SSRI) use. Methods: A multicenter retrospective study included 2000 patients undergoing non-abdominal surgery. The cohort was divided into training (n=1050), internal testing (n=450), and external validation (n=500) cohorts. A dual-algorithm feature selection strategy combining LASSO and Boruta was used to identify robust predictors. Eight machine learning algorithms were developed and compared. Model performance was evaluated using the Area Under the Curve (AUC), calibration plots, and Decision Curve Analysis. Results: Seven independent predictors were identified: chronic SSRI use, history of depression, intraoperative opioids, duration of surgery, neutrophil-to-lymphocyte ratio, serum albumin, and fluid balance. The Random Forest model demonstrated superior discrimination, achieving an AUC of 0.942 in the training cohort, 0.917 in the internal testing cohort, and 0.895 in the external validation cohort. It significantly outperformed standard logistic regression (p<0.05) and displayed excellent calibration. Decision curve analysis indicated a high net clinical benefit, while SHAP analysis visually confirmed the substantial contribution of brain-gut axis factors to delayed bowel recovery. Conclusion: The Random Forest model provides a robust and generalizable tool for predicting POI in non-abdominal surgery patients. By highlighting the critical influence of the brain-gut axis, this study offers new insights for risk stratification and personalized perioperative management.