Machine Learning Algorithm to Predict Worsening of Flexion Range of Motion After Total Knee Arthroplasty.

ABSTRACT:

Background

Predicting the worsening of flexion range of motion (ROM) during the course post-total knee arthroplasty (TKA) is clinically meaningful. This study aimed to create a model that could predict the worsening of knee flexion ROM during the TKA course using a machine learning algorithm and to examine its accuracy and predictive variables.

Methods

Altogether, 344 patients (508 knees) who underwent TKA were enrolled. Knee flexion ROM worsening was defined as ROM decrease of ≥10° between 1 month and 6 months post-TKA. A predictive model for worsening was investigated using 31 variables obtained retrospectively. 5 data sets were created using stratified 5-fold cross-validation. Total data (n = 508) were randomly divided into training (n = 407) and test (n = 101) data. On e

SUBMITTER: Saiki Y 

PROVIDER: S-EPMC9420425 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature