Machine Learning Model Developed to Aid in Patient Selection for Outpatient Total Joint Arthroplasty.

ABSTRACT:

Background

Patient selection for outpatient total joint arthroplasty (TJA) is important for optimizing patient outcomes. This study develops machine learning models that may aid in patient selection for outpatient TJA based on medical comorbidities and demographic factors.

Methods

This study queried elective total knee arthroplasty (TKA) and total hip arthroplasty (THA) cases during 2010-2018 in the American College of Surgeons National Surgical Quality Improvement Program. Artificial neural network models predicted same-day discharge and length of stay (LOS) fewer than 2 days (short LOS). Multiple linear and logistic regression analyses were used to identify variables significantly associated with predicted outcomes.

Results

A total of 284,731 TKA cases and 153,053 T

SUBMITTER: Lopez CD 

PROVIDER: S-EPMC8666332 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature