The Use of Classification Trees to Determine Criteria Leading to a Total Joint Replacement Recommendation for Patients with Knee or Hip Osteoarthritis

Abstract

There is a need for a nonacceptable symptom state to indicate when a patient with knee or hip osteoarthritis exhibits symptoms severe enough to warrant total joint replacement (TJR). A previous study using logistic regression and ROC curve analysis was unable to determine pain and functional disability cut points leading to a TJR recommendation. Using the datasets from the previous study, classification trees were used to identify predictors and cut points of those predictors leading to a TJR recommendation. From the analysis, a patient's quality of life and joint space narrowing appeared to be the most important predictors, out of those included in the analysis, of a surgeon's recommendation for TJR. Further research and analysis is needed to determine if the generated classification trees accurately predict a surgeon's recommendation for TJR.