It is well-documented in the orthopedic literature that 1 in 5 patients are dissatisfied following total knee arthroplasty (TKA). However, multiple statistical models have failed to explain the causes of dissatisfaction. Furthermore, payers are interested in using patient-reported satisfaction scores to adjust surgeon reimbursement rates without a full understanding of the influencing parameters. The purpose of this study was to more comprehensively identify predictors of satisfaction and compare results using both a statistical model and a machine learning (ML) algorithm.
A retrospective review of consecutive TKAs performed by 2 surgeons was conducted. Identical perioperative protocols were utilized by both surgeons. Patients were grouped as satisfied or unsatisfied based on self-reported satisfaction scores. Fifteen variables were correlated with satisfaction using binary logistic regression and stochastic gradient boosted ML models.
In total, 1325 consecutive TKAs were performed. After exclusions, 897 TKAs were available with minimum 1-year follow-up. Overall, 85.3% of patients were satisfied. Older age generation and performing surgeon were predictors of satisfaction in both models. The ML model also retained cruciate-retaining/condylar-stabilizing implant; lack of inflammatory conditions, preoperative narcotic use, depression, and lumbar spine pain; female gender; and a preserved posterior cruciate ligament as predictors of satisfaction which allowed for a significantly higher area under the receiver operator characteristic curve compared to the binary logistic regression model (0.81 vs 0.60).
Therapeutic Level III.
Findings indicate that patient satisfaction may be multifactorial with some factors beyond the scope of a surgeon's control. Further study is warranted to investigate predictors of patient satisfaction particularly with awareness of differences in results between traditional statistical models and ML algorithms.