Logistic Regression and Machine Learning Models Cannot Discriminate Between Satisfied and Dissatisfied Total Knee Arthroplasty Patients.

Abstract Background Approximately 20% of total knee arthroplasty (TKA) patients are found to be dissatisfied or unsure of their satisfaction at 1-year post-surgery. This study attempted to predict 1-year post-surgery dissatisfied/unsure TKA patients with pre-surgery and surgical variables using logistic regression and machine learning methods. Methods A retrospective analysis of patients who underwent primary total knee arthroplasty (TKA) for osteoarthritis between 2012 and 2016 at a single institution was completed. Patients were split into satisfied and dissatisfied/unsure groups. Potential predictor variables included: demographic information, patella re-surfaced, posterior collateral ligament sacrificed and subscales from the Knee Society Knee Scoring System, the Knee Society Clinical Rating System, the Western Ontario and McMaster Universities Osteoarthritis Index, and the 12-Item Short Form Health Survey version 2. Logistic regression and six different machine learning methods were used to create prediction models. Model performance was evaluated using discrimination (AUC) and calibration (Brier score, Cox intercept, and Cox slope) metrics. Results There were 1432 eligible patients included in the analysis, 313 were considered to be dissatisfied/unsure. When evaluating discrimination, the logistic regression (AUC=0.736) and extreme gradient boosted trees (AUC=0.713) models performed best. When evaluating calibration, the logistic regression (Brier score=0.141, Cox intercept=0.241, and Cox slope=1.31) and gradient boosted trees (Brier score=0.149, Cox intercept=0.054, and Cox slope=1.158) models performed best. Conclusion The models developed in this study do not perform well enough as discriminatory tools to be used in a clinical setting. Further work needs to be done to improve the performance of pre-surgery TKA dissatisfaction prediction models.