Aims

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

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

This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images.

Aims

Navigation devices are designed to improve a surgeon’s accuracy in positioning the acetabular and femoral components in total hip arthroplasty (THA). The purpose of this study was to both evaluate the accuracy of an optical computer-assisted surgery (CAS) navigation system and determine whether preoperative spinopelvic mobility (categorized as hypermobile, normal, or stiff) increased the risk of acetabular component placement error.

Aims. There is increasing evidence to support the use of topical antibiotics to prevent surgical site infections. Although previous research suggests a minimal nephrotoxic risk with a single dose of vancomycin powder, fracture patients often require multiple procedures and receive additional doses of topical antibiotics. We aimed to determine if cumulative doses of intrawound vancomycin or tobramycin powder for infection prophylaxis increased the risk of drug-induced acute kidney injury (AKI) among fracture patients. Methods. This cohort study was a secondary analysis of single-centre Program of Randomized Trials to Evaluate Pre-operative Antiseptic Skin Solutions in Orthopaedic Trauma (PREP-IT) trial data. We included patients with a surgically treated appendicular fracture. The primary outcome was drug-induced AKI. The odds of AKI per gram of vancomycin or tobramycin powder were calculated using Bayesian regression models, which adjusted for measured confounders and accounted for the interactive effects of vancomycin and tobramycin. Results. Of the 782 included patients (mean age 48 years (SD 20); 59% male), 83% (n = 648) received at least one vancomycin dose (cumulative range 1 to 12 g). Overall, 45% of the sample received at least one tobramycin dose (cumulative range 1.2 to 9.6 g). Drug-induced AKI occurred in ten patients (1.2%). No association was found between the cumulative dose of vancomycin and drug-induced AKI (odds ratio (OR) 1.08 (95% credible interval (CrI) 0.52 to 2.14)). Additional doses of tobramycin were associated with a three-fold increase in the adjusted odds of drug-induced AKI (OR 3.66 (95% CrI 1.71 to 8.49)). Specifically, the risk of drug-induced AKI rose substantially after 4.8 g of tobramycin powder (7.5% (95% CrI 1.0 to 35.3)). Conclusion. Cumulative doses of vancomycin were not associated with an increased risk of drug-induced AKI among fracture patients. While the risk of drug-induced AKI remains less than 4% with three or fewer 1.2 g tobramycin doses, the estimated risk increases substantially to 8% after four cumulative doses. Level of evidence: Therapeutic Level III. Cite this article: Bone Jt Open 2022;3(4):284–290

Lung cancer is the most common cancer diagnosed, the leading cause of cancer-related deaths, and bone metastases occurs in 20–40% of lung cancer patients. They often present symptomatically with pain or skeletal related events (SREs), which are independently associated with decreased survival. Bone modifying agents (BMAs) such as Denosumab or bisphosphonates are routinely used, however no specific guidelines exist from the National Comprehensive Cancer Center or the European Society of Medical Oncologists. Perhaps preventing the formation of guidelines is the lack of a high-quality quantitative synthesis of randomized controlled trial (RCT) data to determine the optimal treatment for the patient important outcomes of 1) Overall survival (OS), 2) Time to SRE, 3) SRE incidence, and 4) Pain Resolution. The objective of this study was to perform the first systematic review and network meta-analysis (NMA) to assess the best BMA for treatment of metastatic lung cancer to bone. We conducted our study in accordance to the PRISMA protocol. We performed a librarian assisted search of MEDLINE, PubMed, EMBASE, and Cochrane Library and Chinese databases including CNKI and Wanfang Data. We included studies that are RCTs reporting outcomes specifically for lung cancer patients treated with a bisphosphonate or Denosumab. Screening, data extraction, risk of bias and GRADE were performed in duplicate. The NMA was performed using a Bayesian probability model with R. Results are reported as relative risks, odds ratios or mean differences, and the I2 value is reported for heterogeneity. We assessed all included articles for risk of bias and applied the novel GRADE framework for NMAs to rate the quality of evidence supporting each outcome. We included 132 RCTs comprising 11,161 patients with skeletal metastases from lung cancer. For OS, denosumab was ranked above zoledronic acid (ZA) and estimated to confer an average of 3.7 months (95%CI: −0.5 – 7.6) increased survival compared to untreated patients. For time to SRE, denosumab was ranked first with an average of 9.1 additional SRE-free months (95%CI: 4.0 – 14.0) compared to untreated patients, while ZA conferred an additional 4.8 SRE-free months (2.4 – 7.0). Patients treated with the combination of Ibandronate and systemic therapy were 2.3 times (95%CI: 1.7 – 3.2) more likely to obtain successful pain resolution, compared to untreated. Meta-regression showed no effect of heterogeneity length of follow-up or pain scales on the observed treatment effects. Heterogeneity in the network was considered moderate for overall survival and time to SRE, mild for SRE incidence, and low for pain resolution. While a generally high risk of bias was observed across studies, whether they were from Western or Chinese databases. The overall GRADE for the evidence underlying our results is High for Pain control and SRE incidence, and Moderate for OS and time to SRE. This study represents the most comprehensive synthesis of the best available evidence guiding pharmacological treatment of bone metastases from lung cancer. Denosumab is ranked above ZA for both overall survival and time to SRE, but both treatments are superior to no treatment. ZA was first among all bisphosphonates assessed for odds of reducing SRE incidence, while the combination of Ibandronate and radionuclide therapy was most effective at significantly reducing pain from metastases. Clinicians and policy makers may use this synthesis of all available RCT data as support for the use of a BMA in MBD for lung cancer

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

Background. The advent of value-based conscientiousness and rapid-recovery discharge pathways presents surgeons, hospitals, and payers with the challenge of providing the same total hip arthroplasty episode of care in the safest and most economic fashion for the same fee, despite patient differences. Various predictive analytic techniques have been applied to medical risk models, such as sepsis risk scores, but none have been applied or validated to the elective primary total hip arthroplasty (THA) setting for key payment-based metrics. The objective of this study was to develop and validate a predictive machine learning model using preoperative patient demographics for length of stay (LOS) after primary THA as the first step in identifying a patient-specific payment model (PSPM). Methods. Using 229,945 patients undergoing primary THA for osteoarthritis from an administrative database between 2009– 16, we created a naïve Bayesian model to forecast LOS after primary THA using a 3:2 split in which 60% of the available patient data “built” the algorithm and the remaining 40% of patients were used for “testing.” This process was iterated five times for algorithm refinement, and model performance was determined using the area under the receiver operating characteristic curve (AUC), percent accuracy, and positive predictive value. LOS was either grouped as 1–5 days or greater than 5 days. Results. The machine learning model algorithm required age, race, gender, and two comorbidity scores (“risk of illness” and “risk of morbidity”) to demonstrate excellent validity, reliability, and responsiveness with an AUC of 0.87 after five iterations. Hospital stays of greater than 5 days for THA were most associated with increased risk of illness and risk of comorbidity scores during admission compared to 1–5 days of stay. Conclusions. Our machine learning model derived from administrative big data demonstrated excellent validity, reliability, and responsiveness after primary THA while accurately predicting LOS and identifying two comorbidity scores as key value-based metrics. Predictive data has the potential to engender a risk-based PSPM prior to primary THA and other elective orthopaedic procedures

Objectives

Alarm over the reported high failure rates for metal-on-metal (MoM) hip implants as well as their potential for locally aggressive Adverse Reactions to Metal Debris (ARMDs) has prompted government agencies, internationally, to recommend the monitoring of patients with MoM hip implants. Some have advised that a blood ion level >7 µg/L indicates potential for ARMDs. We report a systematic review and meta-analysis of the performance of metal ion testing for ARMDs.

Various injury severity scores exist for trauma; it is known that they do not correlate accurately to military injuries. A promising anatomical scoring system for blast pelvic and perineal injury led to the development of an improved scoring system using machine-learning techniques. An unbiased genetic algorithm selected optimal anatomical and physiological parameters from 118 military cases. A Naïve Bayesian (NB) model was built using the proposed parameters to predict the probability of survival. Ten-fold cross validation was employed to evaluate its performance. Our model significantly out-performed Injury Severity Score (ISS), Trauma ISS, New ISS and the Revised Trauma Score in virtually all areas; Positive Predictive Value 0.8941, Specificity 0.9027, Accuracy 0.9056 and Area Under Curve 0.9059. A two-sample t-test showed that the predictive performance of the proposed scoring system was significantly better than the other systems (p<0.001). With limited resources and the simplest of Bayesian methodologies we have demonstrated that the Naïve Bayesian model performed significantly better in virtually all areas assessed by current scoring systems used for trauma. This is encouraging and highlights that more can be done to improve trauma systems not only for the military, but also in civilian trauma

The December 2013 Hip & Pelvis Roundup³⁶⁰ looks at: Enhanced recovery works; Acetabular placement; Exercise better than rest in osteoarthritis patients; if Birmingham hip resurfacing is immune from pseudotumour; HIV and arthroplasty; Labral tears revisited; Prophylactic surgery for FAI; and Ceramics and impaction grafting

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.