Preoperative Nutrition–Inflammation Status and Surgical Factors Predict Delayed Extubation After Oral Cancer Surgery with Free Flap Reconstruction: A Machine Learning Approach

Background Delayed extubation (DE) is a common postoperative challenge after oral cancer resection with free flap reconstruction. Existing risk assessments largely focus on anatomical and surgical factors, with limited consideration of patients’ systemic physiological status. The Advanced Lung Cancer Inflammation Index (ALI), a preoperative composite measure of nutritional and inflammatory status, has shown prognostic value in oncology but its role in perioperative airway management is unclear. Methods We retrospectively analyzed 752 patients undergoing oral cancer resection with free flap reconstruction at a single center. Associations between preoperative ALI and DE were evaluated using multivariable logistic regression. A random forest model integrating ALI with clinical and surgical factors was developed and interpreted using SHapley Additive exPlanations (SHAP) to identify key predictors. Results DE occurred in 32.2% of patients. Higher preoperative ALI was independently associated with lower risk of DE (adjusted OR per 10-point increase = 0.90, 95% CI: 0.84–0.95, p < 0.001). The random forest model achieved an AUC of 0.875 in the validation cohort and demonstrated good calibration. SHAP analysis revealed tumor T stage, extent of resection, bilateral neck dissection, and ALI as the most influential predictors, with higher ALI consistently protective. Conclusions Preoperative ALI is an independent predictor of delayed extubation. An interpretable machine learning model combining ALI with clinical and surgical variables provides a high-performing tool for individualized perioperative airway risk assessment, supporting tailored extubation strategies and postoperative management in oral cancer patients.