Predictors of Patient Satisfaction Following Primary Total Knee Arthroplasty: Results from a Traditional Statistical Model and a Machine Learning Algorithm

Hassan Farooq, Evan R. Deckard, Mary Ziemba-Davis, Adam Madsen, R. Michael Meneghini

Research output: Contribution to journalArticle

Abstract

Background: 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. Methods: 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. Results: 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). Conclusion: 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. Level of Evidence: Therapeutic Level III.

Original languageEnglish (US)
JournalJournal of Arthroplasty
DOIs
StateAccepted/In press - 2020

Keywords

  • binary logistic regression
  • machine learning
  • predictors
  • satisfaction
  • total knee arthroplasty

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine

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