Predicting Dissatisfaction Following Total Hip Arthroplasty Using A Bayesian Model Averaging Approach: Results From The Australian Arthroplasty Clinical Outcomes Registry National (Acorn)

ANZ JOURNAL OF SURGERY(2021)

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摘要
BackgroundResults Total hip arthroplasty (THA) provides excellent pain relief and improved function in patients with painful arthritis. The aim of this study was to identify rates and predictors of dissatisfaction following THA. Methods Data were collected prospectively from the Australian Arthroplasty Clinical Outcomes Registry National (ACORN) database between 2014 and 2016 from 2096 patients who underwent THA. Data included baseline demographics, patient-reported outcome measures (PROMs) and postoperative clinical outcomes. Patients were dichotomized into two groups based on their 6-month response to the satisfaction question answered on a Likert scale. Eighteen predefined variables were analyzed. PROMs included full Oxford Hip Score, EQ-5D, and patient satisfaction. A Bayesian model averaging approach was used to build the best predictive model for dissatisfaction. Multiple logistic regression techniques were applied to quantify the effect size of the best model. At 6 months following THA, 95.4% of patients (n = 2000) were satisfied with surgical outcome and 4.6% (n = 96) were dissatisfied. The only variable that was significantly associated with dissatisfaction after THA was "complications after discharge." This result was consistent for both the complete and imputed dataset (odds ratio 4.78, 95% confidence interval 2.60-8.80, P < 0.001 and odds ratio 3.8, 95% confidence interval 2.60-5.60, P < 0.001, respectively). Conclusion Our study confirms the high rates of patient satisfaction following THA, with postoperative complications being the only determinant of dissatisfaction. Optimization of patients prior to surgery, reducing postoperative complications, may further improve satisfaction rates after THA.
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关键词
dissatisfaction, patient-reported outcome measure, satisfaction, total hip arthroplasty
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