Predicting periprosthetic joint infection in primary total knee arthroplasty: a machine learning model integrating preoperative and perioperative risk factors.

ABSTRACT:

Background

Periprosthetic joint infection leads to significant morbidity and mortality after total knee arthroplasty. Preoperative and perioperative risk prediction and assessment tools are lacking in Asia. This study developed the first machine learning model for individualized prediction of periprosthetic joint infection following primary total knee arthroplasty in this demographic.

Methods

A retrospective analysis was conducted on 3,483 primary total knee arthroplasty (81 with periprosthetic joint infection) from 1998 to 2021 in a Chinese tertiary and quaternary referral academic center. We gathered 60 features, encompassing patient demographics, operation-related variables, laboratory findings, and comorbidities. Six of them were selected after univariate and multivariat

SUBMITTER: Chong YY 

PROVIDER: S-EPMC11895328 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature