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

ABSTRACT:

Background

The rising global demand for total knee arthroplasty (TKA) has accelerated the shift toward ambulatory surgery, aimed at same-day or next-day discharge. However, significant variability in discharge protocols and high rates of delayed discharge in unselected patients challenge its widespread implementation. This study develops an interpretable machine learning framework to preemptively identify risk factors for delayed discharge in ambulatory TKA.

Methods

This retrospective study analyzed data from 449 patients who underwent ambulatory total knee arthroplasty between September 2021 and June 2024. Fourteen machine learning models were developed and validated using preoperative variables selected via LASSO and multivariate regression. The dataset was split into trai

SUBMITTER: Feng J 

PROVIDER: S-EPMC12626822 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature