Development of a machine learning algorithm to identify surgical candidates for hip and knee arthroplasty without in-person evaluation.

ABSTRACT:

Introduction

Arthroplasty care delivery is facing a growing supply-demand mismatch. To meet future demand for joint arthroplasty, systems will need to identify potential surgical candidates prior to evaluation by orthopaedic surgeons.

Materials and methods

Retrospective review was conducted at two academic medical centers and three community hospitals from March 1 to July 31, 2020 to identify new patient telemedicine encounters (without prior in-person evaluation) for consideration of hip or knee arthroplasty. The primary outcome was surgical indication for joint replacement. Five machine learning algorithms were developed to predict likelihood of surgical indication and assessed by discrimination, calibration, overall performance, and decision curve analysis.

Results

SUBMITTER: Crawford AM 

PROVIDER: S-EPMC10008010 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature