Machine Learning Model Can Predict Periprosthetic Joint Infection Following Total Knee Arthroplasty
Title: Machine Learning Model Can Predict Periprosthetic Joint Infection Following Total Knee Arthroplasty
by Chong Yuk Yee
Abstract
Introduction: Periprosthetic joint infection (PJI) is a significant complication of primary total knee arthroplasty (TKA). A prediction tool to assist clinical preoperative risk assessment is important. However, no such model is tailored for Hong Kong patients. This study aimed to develop a machine learning (ML)-based model for predicting PJI following primary TKA in Hong Kong.
Materials and Methods: A retrospective analysis was conducted in a local teaching hospital on 3,483 primary TKA (81 with PJI) from 1998 to 2021. We gathered 61 features, encompassing patient demographics, operation-related variables, laboratory findings and comorbidities. Six of them were selected by univariate and multivariate analysis. We trained a Balanced Random Forest classifier using stratified 10-fold cross-validation and compared it with Logistic Regression to verify ML performance.