Aims. The purpose of this investigation was to determine the relationship between height, weight, and sex with implant size in total knee arthroplasty (TKA) using a multivariate linear regression model and a Bayesian model. Methods. A retrospective review of an institutional registry was performed of primary TKAs performed between January 2005 and December 2016. Patient demographics including patient age, sex, height, weight, and body mass index (BMI) were obtained from registry and medical record review. In total, 8,100 primary TKAs were included. The mean age was 67.3 years (SD 9.5) with a mean BMI of 30.4 kg/m. 2. (SD 6.3). The TKAs were randomly split into a training cohort (n = 4,022) and a testing cohort (n = 4,078). A multivariate linear regression model was created on the training cohort and then applied to the testing cohort . A Bayesian model was created based on the frequencies of implant sizes in the training cohort. The model was then applied to the testing cohort to determine the accuracy of the model at 1%, 5%, and 10% tolerance of inaccuracy. Results. Height had a relatively strong correlation with implant size (femoral component anteroposterior (AP) Pearson correlation coefficient (ρ) = 0.73, p < 0.001; tibial component mediolateral (ML) ρ = 0.77, p < 0.001). Weight had a moderately strong correlation with implant size, (femoral component AP ρ = 0.46, p < 0.001; tibial ML ρ = 0.48, p < 0.001). There was a significant linear correlation with height, weight, and sex with implant size (femoral component R. 2. = 0.607, p < 0.001; tibial R. 2. = 0.695, p < 0.001). The Bayesian model showed high accuracy in predicting the range of required implant sizes (94.4% for the femur and 96.6% for the tibia) accepting a 5% risk of inaccuracy. Conclusion. Implant size was correlated with basic demographic variables including height, weight, and sex. The linear regression and Bayesian models accurately predicted required implant sizes across multiple manufacturers based on height, weight, and sex alone. These types of predictive models may help improve operating room and implant supply chain efficiency. Level of Evidence: Level IV. Cite this article: Bone Joint J 2020;102-B(6 Supple A):85–90

Aims. Intra-articular (IA) injection may be used when treating hip osteoarthritis (OA). Common injections include steroids, hyaluronic acid (HA), local anaesthetic, and platelet-rich plasma (PRP). Network meta-analysis allows for comparisons between two or more treatment groups and uses direct and indirect comparisons between interventions. This network meta-analysis aims to compare the efficacy of various IA injections used in the management of hip OA with a follow-up of up to six months. Methods. This systematic review and network meta-analysis used a Bayesian random-effects model to evaluate the direct and indirect comparisons among all treatment options. PubMed, Web of Science, Clinicaltrial.gov, EMBASE, MEDLINE, and the Cochrane Library were searched from inception to February 2023. Randomized controlled trials (RCTs) which evaluate the efficacy of HA, PRP, local anaesthetic, steroid, steroid+anaesthetic, HA+PRP, and physiological saline injection as a placebo, for patients with hip OA were included. Results. In this meta-analysis of 16 RCTs with a total of 1,735 participants, steroid injection was found to be significantly more effective than placebo injection on reported pain at three months, but no significant difference was observed at six months. Furthermore, steroid injection was considerably more effective than placebo injection for functional outcomes at three months, while the combination of HA+PRP injection was substantially more effective at six months. Conclusion. Evidence suggests that steroid injection is more effective than saline injection for the treatment of hip joint pain, and restoration of functional outcomes. Cite this article: Bone Joint J 2024;106-B(6):532–539

Aims

We investigated the efficacy and safety profile of commonly used venous thromboembolism (VTE) prophylaxis agents following hip and knee arthroplasty.

Follow-up radiographs are usually used as the reference standard for the diagnosis of suspected scaphoid fractures. However, these are prone to errors in interpretation. We performed a meta-analysis of 30 clinical studies on the diagnosis of suspected scaphoid fractures, in which agreement data between any of follow-up radiographs, bone scintigraphy, magnetic resonance (MR) imaging, or CT could be obtained, and combined this with latent class analysis to infer the accuracy of these tests on the diagnosis of suspected scaphoid fractures in the absence of an established standard. The estimated sensitivity and specificity were respectively 91.1% and 99.8% for follow-up radiographs, 97.8% and 93.5% for bone scintigraphy, 97.7% and 99.8% for MRI, and 85.2% and 99.5% for CT. The results were generally robust in multiple sensitivity analyses. There was large between-study heterogeneity for the sensitivity of follow-up radiographs and CT, and imprecision about their sensitivity estimates.

If we acknowledge the lack of a reference standard for diagnosing suspected scaphoid fractures, MRI is the most accurate test; follow-up radiographs and CT may be less sensitive, and bone scintigraphy less specific.