There is an increasing incidence of revision for periprosthetic joint infection. The addition of vancomycin to beta-lactam antimicrobial prophylaxis in joint arthroplasty may reduce surgical site infections, however, the efficacy and safety have not been established.

This was a multicenter, double-blind, superiority, placebo-controlled trial. We randomized 4239 adult patients undergoing joint arthroplasty surgery to receive 1.5g vancomycin or normal saline placebo, in addition to standard cefazolin antimicrobial prophylaxis. The primary outcome was surgical site infection at 90-days from index surgery. Perioperative carriage of Staphylococcus species was also assessed.

In the 4113 patients included in the modified intention-to-treat population, surgical site infections occurred in 72/2069 (3.5%) in the placebo group and 91/2044 (4. 5%) in the vancomycin group (risk ratio 1.28; 95% confidence interval 0.94 to 1.73; p value 0.11). No difference was observed between the two groups for primary hip arthroplasty procedures. A higher proportion of infections occurred in knee arthroplasty patients in the vancomycin group (63/1109 [4.7%]) compared with the placebo group (42/1124 [3.7%]; risk ratio 1.52; 95% confidence interval 1.04 to 2.23; p value 0.031). Hypersensitivity reactions occurred in 11 (0.5%) patients in the placebo group and 24 (1.2%) in the vancomycin group (risk ratio 2.20; 95% confidence interval 1.08, 4.49) and acute kidney injury in 74 (3.7%) patients in the placebo group and 42 (2.1%) in the vancomycin group (risk ratio 0.57; 95% confidence interval 0.39, 0.83). Perioperative Staphylococcus aureus carriage was detected in 1089/3748 (29.1%) of patients.

This is the first randomized controlled trial examining the addition of a glycopeptide antimicrobial to standard beta-lactam surgical antimicrobial prophylaxis in joint arthroplasty. The addition of vancomycin to standard cefazolin prophylaxis was not superior to placebo for the prevention of surgical site infections in hip and knee arthroplasty surgery.

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 perceptible change in utility score, the minimal clinically important difference (MCID) can be calculated. However, there are multiple methods to calculate MCID with no consensus about which method is most appropriate. The aim of this study is to calculate MCID values for the Veterans-RAND 12 (VR12) utility score using varying methods. Our hypothesis is that different methods will yield different MCID values.

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.

Objectives

Whilst gait speed is variable between healthy and injured adults, the extent to which speed alone alters the 3D in vivo knee kinematics has not been fully described. The purpose of this prospective study was to understand better the spatiotemporal and 3D knee kinematic changes induced by slow compared with normal self-selected walking speeds within young healthy adults.