Aims

Osteoarthritis (OA) is a widespread chronic degenerative joint disease with an increasing global impact. The pathogenesis of OA involves complex interactions between genetic and environmental factors. Despite this, the specific genetic mechanisms underlying OA remain only partially understood, hindering the development of targeted therapeutic strategies.

Aims. Assessment of bone health is a multifaceted clinical process, incorporating biochemical and diagnostic tests that should be accurate and reproducible. Dual-energy X-ray absorptiometry (DXA) is the reference standard for evaluation of bone mineral density, but has known limitations. Alternatives include quantitative CT (q-CT), MRI, and peripheral quantitative ultrasound (QUS). Radiofrequency echographic multispectrometry (REMS) is a new generation of ultrasound technology used for the assessment of bone mineral density (BMD) at axial sites that is as accurate as quality-assured DXA scans. It also provides an assessment of the quality of bone architecture. This will be of direct value and significance to orthopaedic surgeons when planning surgical procedures, including fracture fixation and surgery of the hip and spine, since BMD alone is a poor predictor of fracture risk. Methods. The various other fixed-site technologies such as high-resolution peripheral q-CT (HR-pQCT) and MRI offer no further significant prognostic advantages in terms of assessing bone structure and BMD to predict fracture risk. QUS was the only widely adopted non-fixed imaging option for bone health assessment, but it is not considered adequately accurate to provide a quantitative assessment of BMD or provide a prediction of fracture risk. In contrast, REMS has a robust evidence base that demonstrates its equivalence to DXA in determining BMD at axial sites. Fracture prediction using REMS, combining the output of fragility information and BMD, has been established as more accurate than when using BMD alone. Conclusion. The practice parameters described in this protocol provide a framework for clinicians who provide REMS services that will, to the greatest possible extent, ensure the most accurate assessment possible from this diagnostic technology. Cite this article: Bone Jt Open 2025;6(3):291–297

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Prevalence of artificial intelligence (AI) algorithms within the Trauma & Orthopaedics (T&O) literature has greatly increased over the last ten years. One increasingly explored aspect of AI is the automated interpretation of free-text data often prevalent in electronic medical records (known as natural language processing (NLP)). We set out to review the current evidence for applications of NLP methodology in T&O, including assessment of study design and reporting.

Aims. Adult spinal deformity (ASD) surgery can reduce pain and disability. However, the actual surgical efficacy of ASD in doing so is far from desirable, with frequent complications and limited improvement in quality of life. The accurate prediction of surgical outcome is crucial to the process of clinical decision-making. Consequently, the aim of this study was to develop and validate a model for predicting an ideal surgical outcome (ISO) two years after ASD surgery. Methods. We conducted a retrospective analysis of 458 consecutive patients who had undergone spinal fusion surgery for ASD between January 2016 and June 2022. The outcome of interest was achievement of the ISO, defined as an improvement in patient-reported outcomes exceeding the minimal clinically important difference, with no postoperative complications. Three machine-learning (ML) algorithms – LASSO, RFE, and Boruta – were used to identify key variables from the collected data. The dataset was randomly split into training (60%) and test (40%) sets. Five different ML models were trained, including logistic regression, random forest, XGBoost, LightGBM, and multilayer perceptron. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Results. The analysis included 208 patients (mean age 64.62 years (SD 8.21); 48 male (23.1%), 160 female (76.9%)). Overall, 42.8% of patients (89/208) achieved the ideal surgical outcome. Eight features were identified as key variables affecting prognosis: depression, osteoporosis, frailty, failure of pelvic compensation, relative functional cross-sectional area of the paraspinal muscles, postoperative sacral slope, pelvic tilt match, and sagittal age-adjusted score match. The best prediction model was LightGBM, achieving the following performance metrics: AUROC 0.888 (95% CI 0.810 to 0.966); accuracy 0.843; sensitivity 0.829; specificity 0.854; positive predictive value 0.806; and negative predictive value 0.872. Conclusion. In this prognostic study, we developed a machine-learning model that accurately predicted outcome after surgery for ASD. The model is built on routinely modifiable indicators, thereby facilitating its integration into clinical practice to promote optimized decision-making. Cite this article: Bone Joint J 2025;107-B(3):337–345

Aims. We aimed to develop and validate a novel prediction model for osteoporosis based on serotonin, fat-soluble vitamins, and bone turnover markers to improve prediction accuracy of osteoporosis. Methods. Postmenopausal women aged 55 to 65 years were recruited and divided into three groups based on DXA (normal, osteopenia, and osteoporosis). A total of 109 participants were included in this study and split into healthy (39/109, 35.8%), osteopenia (35/109, 32.1%), and osteoporosis groups (35/109, 32.1%). Serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers of participants were measured. Stepwise discriminant analysis was performed to identify efficient predictors for osteoporosis. The prediction model was developed based on Bayes and Fisher’s discriminant functions, and validated via leave-one-out cross-validation. Normal and empirical volume under the receiver operating characteristic (ROC) surface (VUS) tests were used to evaluate predictive effects of variables in the prediction model. Results. Significant variables including oestrogen (E2), total procollagen type 1 amino-terminal propeptide (TP1NP), parathyroid hormone (PTH), BMI, vitamin K, serotonin, osteocalcin (OSTEOC), vitamin A, and vitamin D3 were used for the development of the prediction model. The training accuracy for normal, osteopenia, and osteoporosis is 74.4% (29/39), 80.0% (28/35), and 85.7% (30/35), respectively, while the total training accuracy is 79.8% (87/109). The internal validation showed excellent performance with 72.5% testing accuracy (72/109). Among these variables, serotonin and vitamin K exert important roles in the prediction of osteoporosis. Conclusion. We successfully developed and validated a novel prediction model for osteoporosis based on serum concentrations of serotonin, fat-soluble vitamins, and bone turnover markers. In addition, interactive communication between serotonin and fat-soluble vitamins was observed to be critical for bone health in this study. Cite this article: Bone Joint Res 2025;14(2):111–123

Aims. Machine learning (ML) holds significant promise in optimizing various aspects of total shoulder arthroplasty (TSA), potentially improving patient outcomes and enhancing surgical decision-making. The aim of this systematic review was to identify ML algorithms and evaluate their effectiveness, including those for predicting clinical outcomes and those used in image analysis. Methods. We searched the PubMed, EMBASE, and Cochrane Central Register of Controlled Trials databases for studies applying ML algorithms in TSA. The analysis focused on dataset characteristics, relevant subspecialties, specific ML algorithms used, and their performance outcomes. Results. Following the final screening process, 25 articles satisfied the eligibility criteria for our review. Of these, 60% focused on tabular data while the remaining 40% analyzed image data. Among them, 16 studies were dedicated to developing new models and nine used transfer learning to leverage existing pretrained models. Additionally, three of these models underwent external validation to confirm their reliability and effectiveness. Conclusion. ML algorithms used in TSA demonstrated fair to good performance, as evidenced by the reported metrics. Integrating these models into daily clinical practice could revolutionize TSA, enhancing both surgical precision and patient outcome predictions. Despite their potential, the lack of transparency and generalizability in many current models poses a significant challenge, limiting their clinical utility. Future research should prioritize addressing these limitations to truly propel the field forward and maximize the benefits of ML in enhancing patient care. Cite this article: Bone Jt Open 2025;6(2):126–134

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To establish cut-off values for lateral pubofemoral distance (PFD) measurements for detecting hip dysplasia in early (four days) and standard care (six weeks) screening for developmental dysplasia of the hip (DDH).

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The aims of this study, using a porcine model of multiple trauma, were to investigate the expression of microRNAs at the fracture site, in the fracture haematoma (fxH) and in the fractured bone, compared with a remote unfractured long bone, to characterize the patterns of expression of circulating microRNAs in plasma, and identify and validate messenger RNA (mRNA) targets of the microRNAs.

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The aim of this study was to evaluate the suitability, against an accepted international standard, of a linked hip fracture registry and routinely collected administrative dataset in England to embed and deliver randomized controlled trials (RCTs).

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Worldwide controversy exists on the optimal treatment of stable dysplastic hips. The most common treatment options are abduction brace treatment and active surveillance. The primary aim of this study was to assess the effect of active surveillance in stable hip dysplasia, by investigating the percentage of Graf IIb stable dysplastic hips that recover spontaneously without abduction brace treatment. The second aim was to identify prognostic factors for spontaneous recovery of stable dysplastic hips.

The February 2025 Spine Roundup³⁶⁰ looks at: The effect of thoraco-lumbo-sacral orthosis wear time and clinical risk factors on curve progression for individuals with adolescent idiopathic scoliosis; Does operative level impact dysphagia severity after anterior cervical discectomy and fusion? A multicentre prospective analysis; Who gets better after surgery for degenerative cervical myelopathy? A responder analysis from the multicentre Canadian spine outcomes and research network; Do obese patients have worse outcomes in adult spinal deformity surgeries?; An update to the management of spinal cord injury; Classifying thoracolumbar injuries; High- versus moderate-density constructs in adolescent idiopathic scoliosis are equivalent at two years; Romosozumab for protecting against proximal junctional kyphosis in deformity surgery.

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The aim of this study was to develop and evaluate a deep learning-based model for classification of hip fractures to enhance diagnostic accuracy.

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While cementless fixation offers potential advantages over cemented fixation, such as a shorter operating time, concerns linger over its higher cost and increased risk of periprosthetic fractures. If the risk of fracture can be forecasted, it would aid the shared decision-making process related to cementless stems. Our study aimed to develop and validate predictive models of periprosthetic femoral fracture (PPFF) necessitating revision and reoperation after elective total hip arthroplasty (THA).

Aims. Better prediction of outcome after total hip arthroplasty (THA) is warranted. Systemic inflammation and central neuroinflammation are possibly involved in progression of osteoarthritis and pain. We explored whether inflammatory biomarkers in blood and cerebrospinal fluid (CSF) were associated with clinical outcome, and baseline pain or disability, 12 months after THA. Methods. A total of 50 patients from the Danish Pain Research Biobank (DANPAIN-Biobank) between January and June 2018 were included. Postoperative outcome was assessed as change in Oxford Hip Score (OHS) from baseline to 12 months after THA, pain was assessed on a numerical rating scale, and disability using the Pain Disability Index. Multiple regression models for each clinical outcome were included for biomarkers in blood and CSF, respectively, including age, sex, BMI, and Kellgren-Lawrence score. Results. Change in OHS was associated with blood concentrations of tumour necrosis factor (TNF), interleukin-8 (IL-8), interleukin-6 receptor (IL-6R), glycoprotein 130 (gp130), and IL-1β (R. 2. = 0.28, p = 0.006), but not with CSF biomarkers. Baseline pain was associated with blood concentrations of lymphotoxin alpha (LTα), TNFR1, TNFR2, and IL-6R (R. 2. = 0.37, p < 0.001) and CSF concentrations of TNFR1, TNFR2, IL-6, IL-6R, and IL-1Ra (R. 2. = 0.40, p = 0.001). Baseline disability was associated with blood concentrations of TNF, LTα, IL-8, IL-6, and IL-1α (R. 2. = 0.53, p < 0.001) and CSF concentrations of gp130, TNF, and IL-1β (R. 2. = 0.26, p = 0.002). Thus, preoperative systemic low-grade inflammation predicted 12-month postoperative outcome after THA, and was associated with preoperative pain and disability. Conclusion. This study highlights the importance of systemic inflammation in osteoarthritis, and presents a possible path for better patient selection for THA in the future. Preoperative central neuroinflammation was associated with preoperative pain and disability, but not change in OHS after THA. Cite this article: Bone Joint Res 2024;13(12):741–749

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Lower limb reconstruction (LLR) has a profound impact on patients, affecting multiple areas of their lives. Many patient-reported outcome measures (PROMs) are employed to assess these impacts; however, there are concerns that they do not adequately capture all outcomes important to patients, and may lack content validity in this context. This review explored whether PROMs used with adults requiring, undergoing, or after undergoing LLR exhibited content validity and adequately captured outcomes considered relevant and important to patients.

The December 2024 Research Roundup³⁶⁰ looks at: Skeletal muscle composition, power, and mitochondrial energetics in older men and women with knee osteoarthritis; Machine-learning models to predict osteonecrosis in patients with femoral neck fractures undergoing internal fixation; Aetiology of patient dissatisfaction following primary total knee arthroplasty in the era of robotic-assisted technology; Efficacy and safety of commonly used thromboprophylaxis agents following hip and knee arthroplasty; The COVID-19 effect continues; Nickel allergy in knee arthroplasty: does self-reported sensitivity affect outcomes?; Tranexamic acid use and joint infection risk in total hip and knee arthroplasty.

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Surgeon and patient reluctance to participate are potential significant barriers to conducting placebo-controlled trials of orthopaedic surgery. Understanding the preferences of orthopaedic surgeons and patients regarding the design of randomized placebo-controlled trials (RCT-Ps) of knee procedures can help to identify what RCT-P features will lead to the greatest participation. This information could inform future trial designs and feasibility assessments.

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Frailty has been gathering attention as a factor to predict surgical outcomes. However, the association of frailty with postoperative complications remains controversial in spinal metastases surgery. We therefore designed a prospective study to elucidate risk factors for postoperative complications with a focus on frailty.

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Though most humeral shaft fractures heal nonoperatively, up to one-third may lead to nonunion with inferior outcomes. The Radiographic Union Score for HUmeral Fractures (RUSHU) was created to identify high-risk patients for nonunion. Our study evaluated the RUSHU’s prognostic performance at six and 12 weeks in discriminating nonunion within a significantly larger cohort than before.