Utility score is a preference-based measure of general health state – where 0 is equal to death, and 1 is equal to perfect health. To understand a patient's smallest A tertiary institutional registry (SMART) was used as the study cohort. Patients who underwent unilateral TKA for osteoarthritis from January 2012 to January 2020 were included. Utility score was calculated from VR12 responses using the standardised Brazier's method. Distribution and anchor methods were used for the MCID calculation. For distribution methods, 0.5 standard deviations of the baseline and change scores were used. For anchor methods, the physical and emotional anchor questions in the VR12 survey were used to benchmark utility score outcomes. Anchor methods included mean difference in change score, mean difference in 12 month score, and receiver operating characteristics (ROC) analysis with the Youden index. Complete case analysis of 1735 out of 1809 eligible patients was performed. Significant variation in the MCID estimates for VR12 utility score were reported dependent on the calculation method used. The MCID estimate from 0.5 standard deviations of the change score was 0.083. The MCID estimate from the ROC analysis method using physical or emotional anchor question improvement was 0.115 (CI95 0.08-0.14; AUC 0.656). Different MCID calculation methods yielded different MCID values. Our results suggest that MCID is not an umbrella concept but rather many distinct concepts. A general consensus is required to standardise how MCID is defined, calculated, and applied in clinical practice.
Approximately 20% of patients feel unsatisfied 12 months after primary total knee arthroplasty (TKA). Current predictive tools for TKA focus on the clinician as the intended user rather than the patient. The aim of this study is to develop a tool that can be used by patients without clinician assistance, to predict health-related quality of life (HRQoL) outcomes 12 months after total knee arthroplasty (TKA). All patients with primary TKAs for osteoarthritis between 2012 and 2019 at a tertiary institutional registry were analysed. The predictive outcome was improvement in Veterans-RAND 12 utility score at 12 months after surgery. Potential predictors included patient demographics, co-morbidities, and patient reported outcome scores at baseline. Logistic regression and three machine learning algorithms were used. Models were evaluated using both discrimination and calibration metrics. Predictive outcomes were categorised into deciles from 1 being the least likely to improve to 10 being the most likely to improve. 3703 eligible patients were included in the analysis. The logistic regression model performed the best in out-of-sample evaluation for both discrimination (AUC = 0.712) and calibration (gradient = 1.176, intercept = −0.116, Brier score = 0.201) metrics. Machine learning algorithms were not superior to logistic regression in any performance metric. Patients in the lowest decile (1) had a 29% probability for improvement and patients in the highest decile (10) had an 86% probability for improvement. Logistic regression outperformed machine learning algorithms in this study. The final model performed well enough with calibration metrics to accurately predict improvement after TKA using deciles. An ongoing randomised controlled trial (ACTRN12622000072718) is evaluating the effect of this tool on patient willingness for surgery. Full results of this trial are expected to be available by April 2023. A free-to-use online version of the tool is available at smartchoice.org.au.
