Machine learning for predicting extended length of stay in elderly patients with hip fractures: An enhanced recovery after surgery perspective.

ABSTRACT:

Objective

Extended length of stay (eLOS) after hip fracture surgery in elderly patients poses significant clinical and economic challenges. While traditional statistical models identify key predictors, they may miss complex variable interactions. This study compared logistic regression with machine learning (ML) algorithms to predict eLOS, emphasizing actionable factors within Enhanced Recovery After Surgery (ERAS) protocols.

Methods

This retrospective cohort study analyzed 1137 patients aged ≥50 years who underwent hip arthroplasty or internal fixation for hip fracture (2019-2025). Extended LOS was defined as hospital stay ≥14 days based on median LOS of 13.8 days. Two prediction models were developed: preoperative (admission data only) and early in-hospital (including day-

SUBMITTER: Pu H 

PROVIDER: S-EPMC12696291 | biostudies-literature | 2025 Jan-Dec

REPOSITORIES: biostudies-literature