Prediction of MCID achievement for patient satisfaction after TKA using intraoperative data

Abstract

Total Knee Arthroplasty (TKA) is a surgery to replace a deformed joint with an artificial joint due to osteoarthritis. Although the number of surgeries is increasing every year, patient satisfaction after TKA is reported to be 75% to 89%, which is lower than Total Hip Arthroplasty(THA). In this study, we developed and evaluated an algorithm to predict the achievement or non-achievement of the Minimal Clinically Important Difference (MCID) in patient satisfaction (KSS2011) one year after TKA. Three feature selection methods and three machine learning algorithms were evaluated on 62 knees (male: 15, female: 47) that underwent TKA at Nishinomiya Kaisei Hospital and Hyogo Medical University Hospital. As a result, logistic regression showed a maximum Area Under the Curve(AUC) of 0.90. Furthermore, we investigated the importance of features with SHAP. It indicated that preoperative satisfaction and expectancy and femoro-tibial angle (FTA) were important features to predict patient satisfaction.