Which Oxford Knee Score level represents a satisfactory symptom state after undergoing a total knee replacement?

Background and purpose — Meaningful interpretation of postoperative Oxford Knee Score (OKS) levels is challenging. We established Patient Acceptable Symptoms State (PASS) and Treatment Failure (TF) values for the OKS in patients undergoing primary total knee replacement (TKR) in Denmark. Patients and methods — Data from patients undergoing primary TKR between February 2015 and January 2019 was extracted from the arthroplasty registry at the Copenhagen University Hospital, Hvidovre in Denmark. Data included 3, 12, and 24 months postoperative responses to the OKS and 2 anchor questions asking whether they considered their symptom state to be satisfactory, and if not, whether they considered the treatment to have failed. PASS and TF threshold values were calculated using the adjusted predictive modeling method. Non-parametric bootstrapping was used to derive 95% confidence intervals (CI). Results — Complete 3, 12, and 24 months postoperative data was obtained for 187 of 209 (89%), 884 of 915 (97%), and 575 of 586 (98%) patients, with median ages from 68 to 70 years (59 to 64% female). 72%, 77%, and 79% considered as having satisfactory symptoms, while 6%, 11%, and 11% considered the treatment to have failed, at 3, 12, and 24 months postoperatively, respectively. OKS PASS values (CI) were 27 (26–28), 30 (29–31), and 30 (29–31) at 3, 12, and 24 months postoperatively. TF values were 27 (26–28) and 27 (26–29) at 12 and 24 months postoperatively. Interpretation — The OKS PASS values can be used to guide the interpretation of TKR outcome and support quality assessment in institutional and national registries

Interpretation threshold values for the Oxford Knee Score in patients undergoing unicompartmental knee arthroplasty

BACKGROUND AND PURPOSE: Developing meaningful thresholds for the Oxford Knee Score (OKS) advances its clinical use. We determined the minimal important change (MIC), patient acceptable symptom state (PASS), and treatment failure (TF) values as meaningful thresholds for the OKS at 3-, 12-, and 24-month follow-up in patients undergoing unicompartmental knee arthroplasty (UKA). PATIENTS AND METHODS: This is a cohort study with data from patients undergoing UKA collected at a hospital in Denmark between February 2016 and September 2021. The OKS was completed preoperatively and at 3, 12, and 24 months postoperatively. Interpretation threshold values were calculated with the anchor-based adjusted predictive modeling method. Non-parametric bootstrapping was used to derive 95% confidence intervals (CI). RESULTS: Complete 3-, 12-, and 24-month postoperative data was obtained for 331 of 423 (78%), 340 of 479 (71%), and 235 of 338 (70%) patients, median age of 68-69 years (58-59% females). Adjusted OKS MIC values were 4.7 (CI 3.3-6.0), 7.1 (CI 5.2-8.6), and 5.4 (CI 3.4- 7.3), adjusted OKS PASS values were 28.9 (CI 27.6-30.3), 32.7 (CI 31.5-33.9), and 31.3 (CI 29.1-33.3), and adjusted OKS TF values were 24.4 (CI 20.7-27.4), 29.3 (CI 27.3-31.1), and 28.5 (CI 26.0-30.5) at 3, 12, and 24 months postoperatively, respectively. All values statistically significantly increased from 3 to 12 months but not from 12 to 24 months. INTERPRETATION: The UKA-specific measurement properties and clinical thresholds for the OKS can improve the interpretation of UKA outcome and assist quality assessment in institutional and national registries

Minimal important change values for the Oxford Knee Score and the Forgotten Joint Score at 1 year after total knee replacement

Background and purpose — Interpreting changes in Oxford Knee Score (OKS) and Forgotten Joint Score (FJS) following total knee replacement (TKR) is challenged by the lack of methodologically rigorous methods to estimate minimal important change (MIC) values. We determined MIC values by predictive modeling for the OKS and FJS in patients undergoing primary TKR. Patients and methods — We conducted a prospective cohort study in patients undergoing TKR between January 2015 and July 2016. OKS and FJS were completed preoperatively and at 1 year postoperatively, accompanied by a 7-point anchor question ranging from “better, an important improvement” to “worse, an important worsening.” MIC improvement values were defined with the predictive modeling approach based on logistic regression, with patients’ decisions on important improvement as dependent variable and change in OKS/FJS as independent variable. Furthermore, the MICs were adjusted for high proportions of improved patients. Results — 333/496 (67.1%) patients with a median age of 69 years (61% female) had complete data for OKS, FJS, and anchor questions at 1 year postoperatively. 85% were importantly improved. Spearman’s correlations between the anchor and the change score were 0.56 for OKS, and 0.61 for FJS. Adjusted predictive MIC values (95% CI) for improvement were 8 (6–9) for OKS and 14 (10–18) for FJS. Interpretation — The MIC value of 8 for OKS and 14 for FJS corresponds to minimal improvements that the average patient finds important and aids in our understanding of whether improvements after TKR are clinically relevant

Assessing baseline dependency of anchor-based minimal important change (MIC): don’t stratify on the baseline score!

Purpose: The minimal important change (MIC) of a patient-reported outcome measure (PROM) is often suspected to be baseline dependent, typically in the sense that patients who are in a poorer baseline health condition need greater improvement to qualify as minimally important. Testing MIC baseline dependency is commonly performed by creating two or more subgroups, stratified on the baseline PROM score. This study’s purpose was to show that this practice produces biased subgroup MIC estimates resulting in spurious MIC baseline dependency, and to develop alternative methods to evaluate MIC baseline dependency. Methods: Datasets with PROM baseline and follow-up scores and transition ratings were simulated with and without MIC baseline dependency. Mean change MICs, ROC-based MICs, predictive MICs, and adjusted MICs were estimated before and after stratification on the baseline score. Three alternative methods were developed and evaluated. The methods were applied in a real data example for illustration. Results: Baseline stratification resulted in biased subgroup MIC estimates and the false impression of MIC baseline dependency, due to redistribution of measurement error. Two of the alternative methods require a second baseline measurement with the same PROM or another correlated PROM. The third method involves the construction of two parallel tests based on splitting the PROM’s item set. Two methods could be applied to the real data. Conclusion: MIC baseline dependency should not be tested in subgroups based on stratification on the baseline PROM score. Instead, one or more of the suggested alternative methods should be used

Interpretation Threshold Values for the Oxford Hip Score in Patients Undergoing Total Hip Arthroplasty: Advancing Their Clinical Use

Background: Patient-reported outcome measures such as the Oxford Hip Score (OHS) can capture patient-centered perspectives on outcomes after total hip arthroplasty (THA). The OHS assesses hip pain and functional limitations, but defining interpretation threshold values for the OHS is warranted so that numerical OHS values can be translated into whether patients have experienced clinically meaningful changes. Therefore, we determined the minimal important change (MIC), patient acceptable symptom state (PASS), and treatment failure (TF) threshold values for the OHS at 12 and 24-month follow-up in patients undergoing THA. Methods: This cohort study used data from patients undergoing THA at 1 public hospital between July 2016 and April 2021. At 12 and 24 months postoperatively, patients provided responses for the OHS and for 3 anchor questions about whether they had experienced changes in hip pain and function, whether they considered their symptom state to be satisfactory, and if it was not satisfactory, whether they considered the treatment to have failed. The anchor-based adjusted predictive modeling method was used to determine interpretation threshold values. Baseline dependency was evaluated using a new item-split method. Nonparametric bootstrapping was used to determine 95% confidence intervals (CIs). Results: Complete data were obtained for 706 (69%) of 1,027 and 728 (66%) of 1,101 patients at 12 and 24 months postoperatively, respectively. These patients had a median age of 70 years, and 55% to 56% were female. Adjusted OHS MIC values were 6.3 (CI, 4.6 to 8.1) and 5.2 (CI, 3.6 to 6.7), adjusted OHS PASS values were 30.6 (CI, 29.0 to 32.2) and 30.5 (CI, 29.3 to 31.8), and adjusted OHS TF values were 25.5 (CI, 22.9 to 27.7) and 27.0 (CI, 25.2 to 28.8) at 12 and 24 months postoperatively, respectively. MIC values were 5.4 (CI, 2.1 to 9.1) and 5.0 (CI, 1.9 to 8.7) higher at 12 and 24 months, respectively, in patients with a more severe preoperative state. Conclusions: The established interpretation threshold values advance the interpretation and clinical use of the OHS, and may prove especially beneficial for registry-based evaluations of treatment quality