Aims. It is important to understand the rate of complications associated with the increasing burden of revision shoulder arthroplasty. Currently, this has not been well quantified. This review aims to address that deficiency with a focus on complication and reoperation rates, shoulder outcome scores, and comparison of anatomical and reverse prostheses when used in revision surgery. Methods. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review was performed to identify clinical data for patients undergoing revision shoulder arthroplasty. Data were extracted from the literature and pooled for analysis. Complication and reoperation rates were analyzed using a meta-analysis of proportion, and continuous variables underwent comparative subgroup analysis. Results. A total of 112 studies (5,379 shoulders) were eligible for inclusion, although complete clinical data was not ubiquitous. Indications for revision included component loosening 20% (601/3,041), instability 19% (577/3,041), rotator cuff failure 17% (528/3,041), and infection 16% (490/3,041). Intraoperative complication and postoperative complication and reoperation rates were 8% (230/2,915), 22% (825/3,843), and 13% (584/3,843) respectively. Intraoperative and postoperative complications included iatrogenic humeral fractures (91/230, 40%) and instability (215/825, 26%). Revision to reverse total shoulder arthroplasty (TSA), rather than revision to anatomical TSA from any index prosthesis, resulted in lower complication rates and superior Constant scores, although there was no difference in American Shoulder and Elbow Surgeons scores. Conclusion. Satisfactory improvement in patient-reported outcome measures are reported following revision shoulder arthroplasty; however, revision surgery is associated with high complication rates and better outcomes may be evident following revision to reverse TSA. Cite this article: Bone Jt Open 2021;2(8):618–630

Aims. 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. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Aims. The effect of the gut microbiota (GM) and its metabolite on bone health is termed the gut-bone axis. Multiple studies have elucidated the mechanisms but findings vary greatly. A systematic review was performed to analyze current animal models and explore the effect of GM on bone. Methods. Literature search was performed on PubMed and Embase databases. Information on the types and strains of animals, induction of osteoporosis, intervention strategies, determination of GM, assessment on bone mineral density (BMD) and bone quality, and key findings were extracted. Results. A total of 30 studies were included, of which six studies used rats and 24 studies used mice. Osteoporosis or bone loss was induced in 14 studies. Interventions included ten with probiotics, three with prebiotics, nine with antibiotics, two with short-chain fatty acid (SCFA), six with vitamins and proteins, two with traditional Chinese medicine (TCM), and one with neuropeptide Y1R antagonist. In general, probiotics, prebiotics, nutritional interventions, and TCM were found to reverse the GM dysbiosis and rescue bone loss. Conclusion. Despite the positive therapeutic effect of probiotics, prebiotics, and nutritional or pharmaceutical interventions on osteoporosis, there is still a critical knowledge gap regarding the role of GM in rescuing bone loss and its related pathways. Cite this article: Bone Joint Res 2021;10(1):51–59

Osteoarthritis (OA) is mainly caused by ageing, strain, trauma, and congenital joint abnormalities, resulting in articular cartilage degeneration. During the pathogenesis of OA, the changes in subchondral bone (SB) are not only secondary manifestations of OA, but also an active part of the disease, and are closely associated with the severity of OA. In different stages of OA, there were microstructural changes in SB. Osteocytes, osteoblasts, and osteoclasts in SB are important in the pathogenesis of OA. The signal transduction mechanism in SB is necessary to maintain the balance of a stable phenotype, extracellular matrix (ECM) synthesis, and bone remodelling between articular cartilage and SB. An imbalance in signal transduction can lead to reduced cartilage quality and SB thickening, which leads to the progression of OA. By understanding changes in SB in OA, researchers are exploring drugs that can regulate these changes, which will help to provide new ideas for the treatment of OA.

Cite this article: Bone Joint Res 2023;12(9):536–545.

Aims

To systematically review the predominant complication rates and changes to patient-reported outcome measures (PROMs) following osteochondral allograft (OCA) transplantation for shoulder instability.

Aims

Spinopelvic characteristics influence the hip’s biomechanical behaviour. However, to date there is little knowledge defining what ‘normal’ spinopelvic characteristics are. This study aims to determine how static spinopelvic characteristics change with age and ethnicity among asymptomatic, healthy individuals.

Osteoarthritis (OA), one of the most common motor system disorders, is a degenerative disease involving progressive joint destruction caused by a variety of factors. At present, OA has become the fourth most common cause of disability in the world. However, the pathogenesis of OA is complex and has not yet been clarified. Long non-coding RNA (lncRNA) refers to a group of RNAs more than 200 nucleotides in length with limited protein-coding potential, which have a wide range of biological functions including regulating transcriptional patterns and protein activity, as well as binding to form endogenous small interference RNAs (siRNAs) and natural microRNA (miRNA) molecular sponges. In recent years, a large number of lncRNAs have been found to be differentially expressed in a variety of pathological processes of OA, including extracellular matrix (ECM) degradation, synovial inflammation, chondrocyte apoptosis, and angiogenesis. Obviously, lncRNAs play important roles in regulating gene expression, maintaining the phenotype of cartilage and synovial cells, and the stability of the intra-articular environment. This article reviews the results of the latest research into the role of lncRNAs in a variety of pathological processes of OA, in order to provide a new direction for the study of OA pathogenesis and a new target for prevention and treatment.

Cite this article: Bone Joint Res 2021;10(2):122–133.

The ability to edit DNA at the nucleotide level using clustered regularly interspaced short palindromic repeats (CRISPR) systems is a relatively new investigative tool that is revolutionizing the analysis of many aspects of human health and disease, including orthopaedic disease. CRISPR, adapted for mammalian cell genome editing from a bacterial defence system, has been shown to be a flexible, programmable, scalable, and easy-to-use gene editing tool. Recent improvements increase the functionality of CRISPR through the engineering of specific elements of CRISPR systems, the discovery of new, naturally occurring CRISPR molecules, and modifications that take CRISPR beyond gene editing to the regulation of gene transcription and the manipulation of RNA. Here, the basics of CRISPR genome editing will be reviewed, including a description of how it has transformed some aspects of molecular musculoskeletal research, and will conclude by speculating what the future holds for the use of CRISPR-related treatments and therapies in clinical orthopaedic practice.

Cite this article: Bone Joint Res 2020;9(7):351–359.