Aims. The aim of this study was to perform a systematic review and bias evaluation of the current literature to create an overview of risk factors for re-revision following revision total knee arthroplasty (rTKA). Methods. A systematic search of MEDLINE and Embase was completed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. The studies were required to include a population of index rTKAs. Primary or secondary outcomes had to be re-revision. The association between preoperative factors and the effect on the risk for re-revision was also required to be reported by the studies. Results. The search yielded 4,847 studies, of which 15 were included. A majority of the studies were retrospective cohorts or registry studies. In total, 26 significant risk factors for re-revision were identified. Of these, the following risk factors were consistent across multiple studies: age at the time of index revision, male sex, index revision being partial revision, and index revision due to infection. Modifiable risk factors were opioid use, BMI > 40 kg/m. 2. , and anaemia. History of one-stage revision due to infection was associated with the highest risk of re-revision. Conclusion. Overall, 26 risk factors have been associated with an increased risk of re-revision following rTKA. However, various levels of methodological bias were found in the studies. Future studies should ensure valid comparisons by including patients with identical indications and using clear definitions for accurate assessments. Cite this article: Bone Jt Open 2024;5(8):644–651

Aims. The aims of this study were to validate the outcome of total elbow arthroplasty (TEA) in patients with rheumatoid arthritis (RA), and to identify factors that affect the outcome. Methods. We searched PubMed, MEDLINE, Cochrane Reviews, and Embase from between January 2003 and March 2019. The primary aim was to determine the implant failure rate, the mode of failure, and risk factors predisposing to failure. A secondary aim was to identify the overall complication rate, associated risk factors, and clinical performance. A meta-regression analysis was completed to identify the association between each parameter with the outcome. Results. A total of 38 studies including 2,118 TEAs were included in the study. The mean follow-up was 80.9 months (8.2 to 156). The implant failure and complication rates were 16.1% (95% confidence interval (CI) 0.128 to 0.200) and 24.5% (95% CI 0.203 to 0.293), respectively. Aseptic loosening was the most common mode of failure (9.5%; 95% CI 0.071 to 0.124). The mean postoperative ranges of motion (ROMs) were: flexion 131.5° (124.2° to 138.8°), extension 29.3° (26.8° to 31.9°), pronation 74.0° (67.8° to 80.2°), and supination 72.5° (69.5° to 75.5°), and the mean postoperative Mayo Elbow Performance Score (MEPS) was 89.3 (95% CI 86.9 to 91.6). The meta-regression analysis identified that younger patients and implants with an unlinked design correlated with higher failure rates. Younger patients were associated with increased complications, while female patients and an unlinked prosthesis were associated with aseptic loosening. Conclusion. TEA continues to provide satisfactory results for patients with RA. However, it is associated with a substantially higher implant failure and complication rates compared with hip and knee arthroplasties. The patient’s age, sex, and whether cemented fixation and unlinked prosthesis were used can influence the outcome. Level of Evidence: Therapeutic Level IV. Cite this article: Bone Joint J 2020;102-B(8):967–980

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Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials.

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Robotic-assisted unicompartmental knee arthroplasty (R-UKA) has been proposed as an approach to improve the results of the conventional manual UKA (C-UKA). The aim of this meta-analysis was to analyze the studies comparing R-UKA and C-UKA in terms of clinical outcomes, radiological results, operating time, complications, and revisions.

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To systematically review the efficacy of split tendon transfer surgery on gait-related outcomes for children and adolescents with cerebral palsy (CP) and spastic equinovarus foot deformity.

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Giant cell tumour of bone (GCTB) treatment changed since the introduction of denosumab from purely surgical towards a multidisciplinary approach, with recent concerns of higher recurrence rates after denosumab. We evaluated oncological, surgical, and functional outcomes for distal radius GCTB, with a critically appraised systematic literature review.

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The aim of this study was to review the current evidence surrounding curve type and morphology on curve progression risk in adolescent idiopathic scoliosis (AIS).

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A systematic literature review focusing on how long before surgery concurrent viral or bacterial infections (respiratory and urinary infections) should be treated in hip fracture patients, and if there is evidence for delaying this surgery.

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Patients with metabolic syndrome (MetS) are known to be at increased risk of postoperative complications, but it is unclear whether MetS is also associated with complications after total hip arthroplasty (THA) or total knee arthroplasty (TKA). Here, we perform a systematic review and meta-analysis linking MetS to postoperative complications in THA and TKA.

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Metabolic profiling is a top-down method of analysis looking at metabolites, which are the intermediate or end products of various cellular pathways. Our primary objective was to perform a systematic review of the published literature to identify metabolites in human synovial fluid (HSF), which have been categorized by metabolic profiling techniques. A secondary objective was to identify any metabolites that may represent potential biomarkers of orthopaedic disease processes.