Identifying who are unlikely to benefit from total knee arthroplasty using machine learning models.

ABSTRACT: Identifying and preventing patients who are not likely to benefit long-term from total knee arthroplasty (TKA) would decrease healthcare expenditure significantly. We trained machine learning (ML) models (image-only, clinical-data only, and multimodal) among 5720 knee OA patients to predict postoperative dissatisfaction at 2 years. Dissatisfaction was defined as not achieving a minimal clinically important difference in postoperative Knee Society knee and function scores (KSS), Short Form-36 Health Survey [SF-36, divided into a physical component score (PCS) and mental component score (MCS)], and Oxford Knee Score (OKS). Compared to image-only models, both clinical-data only and multimodal models achieved superior performance at predicting dissatisfaction measured by AUC, clinical-data onl

SUBMITTER: Liu X 

PROVIDER: S-EPMC11442804 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature