Hassan Farooq1, Evan R Deckard2, Mary Ziemba-Davis3, Adam Madsen4, R Michael Meneghini5. 1. Indiana University School of Medicine, Indianapolis, IN. 2. Department of Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, IN. 3. IU Health Physicians, Orthopedics & Sports Medicine, IU Health Hip & Knee Center, Fishers, IN. 4. Dr Adam Madsen Orthopedic Surgery, Vernal, UT. 5. Department of Orthopaedic Surgery, Indiana University School of Medicine, Indianapolis, IN; IU Health Physicians, Orthopedics & Sports Medicine, IU Health Hip & Knee Center, Fishers, IN.
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.
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.
Authors: Gary Tran; Lafi S Khalil; Allen Wrubel; Chad L Klochko; Jason J Davis; Steven B Soliman Journal: Skeletal Radiol Date: 2020-11-03 Impact factor: 2.199