A SHAP-interpretable machine learning framework for predicting delayed discharge in ambulatory total knee arthroplasty: comparative validation of 14 models

This study establishes that machine learning, particularly the CATBoost model, effectively predicts delayed discharge in ambulatory total knee arthroplasty using five key preoperative variables. SHAP analysis further enhanced model interpretability by revealing feature interactions, such as the modulating role of ejection fraction. These predictors enable improved risk stratification and personalized discharge planning, supporting optimized resource use and patient management. While limitations like single-center data require cautious interpretation, the findings highlight the potential of predictive analytics for clinical deployment. Further validation in diverse settings is warranted to translate these findings into clinical practice.