Identifying who are unlikely to benefit from total knee arthroplasty using machine learning models
Xiaodi Liu, Yingnan Liu, Mong Li Lee, Wynne Hsu, Ming Han Lincoln Liow
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 only model: KSS 0.888 (0.866-0.909), SF-PCS 0.836 (0.812-0.860), SF-MCS 0.833 (0.812-0.854), and OKS 0.806 (0.753-0.859); multimodal model: KSS 0.891 (0.870-0.911), SF-PCS 0.832 (0.808-0.857), SF-MCS 0.835 (0.811-0.856), and OKS 0.816 (0.768-0.863). Our findings highlighted that ML models using clinical or multimodal data were capable to predict post-TKA dissatisfaction.