Journal
JOURNAL OF ARTHROPLASTY
Volume 37, Issue 7, Pages S479-+Publisher
CHURCHILL LIVINGSTONE INC MEDICAL PUBLISHERS
DOI: 10.1016/j.arth.2022.02.100
Keywords
total hip arthroplasty; patient-reported outcomes; predictive modeling; model-fitting; surgeon
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Surgeon variability has a significant impact on the outcomes of total hip arthroplasty, including patient-reported outcome measures, length of stay, discharge disposition, and readmission rate. Surgeon-level variability appears to be a greater contributor to 1-year patient-reported outcome measures than some patient-level characteristics.
Background: Patient-related and surgery-related factors have been shown to be drivers of outcomes after total hip arthroplasty (THA); however, the impact of intersurgeon variability is poorly understood. The purpose of this study is to assess the following: (1) overall effect of surgeon on 1-year patient-reported outcome measures (PROMs), length of stay (LOS), discharge disposition, and 90-day readmission following THA; and (2) variability in 1-year PROMs among surgeons. Methods: A prospective cohort of 3,695 patients who underwent THA between 2016 and 2018 was included. Seventy-eight percent of patients completed 1-year follow-up. Thirty-one surgeons from a large healthcare system were included. Likelihood ratio tests analyzed the relationship among surgeon and 1-year Hip Disability and Osteoarthritis Outcome Score (HOOS)-Pain, HOOS-Physical Function Short-Form, HOOS-Joint Replacement, University of California, Los Angeles activity score, Patient Acceptable Symptom State, LOS, discharge disposition, and 90-day readmission. Mixed-effect proportional odds and logistic regression models were used to determine variable importance for each outcome. Results: In total, 90.5% of patients responded positively to 1-year Patient Acceptable Symptom State. There was a significant association among surgeon and 1-year PROMs, LOS, discharge disposition (P<.001), and readmission (P=.002). For HOOS-Pain, Physical Function Short-Form, and Joint Replacement, surgeon (Akaike information criterion increase: 34.6, 18.7, 17.1, respectively) was a greater contributor to outcome than patient-level factors, including age, gender, and comorbidity. Differences in the highest and lowest median probability of achieving any given score on 1-year PROMs ranged from 11% to 18.5%. Variability was not explained by approach (P=.431) or case volume (correlation coefficient, rho = 0.19). Conclusion: Surgeon-level variability appears to be a greater driver of 1-year PROMs than some patient-level characteristics. Incorporating surgeon as a variable is beneficial for model-fitting and important for increasing value in THA. (C) 2022 Elsevier Inc. All rights reserved.
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