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. Methods. This systematic review followed the Preferred Reporting Items for Systematic Review and Meta-Analyses guidelines to identify English studies, including ≥ 18-year-old participants, without evidence of hip or spine pathology or a history of previous surgery or interventional treatment, documenting lumbar lordosis (LL), sacral slope (SS), pelvic tilt (PT), and pelvic incidence (PI). From a total of 2,543 articles retrieved after the initial database search, 61 articles were eventually selected for data extraction. Results. When all ethnicities were combined the mean values for LL, SS, PT, and PI were: 47.4° (SD 11.0°), 35.8° (SD 7.8°), 14.0° (SD 7.2°), and 48.8° (SD 10°), respectively. LL, SS, and PT had statistically significant (p < 0.001) changes per decade at: −1.5° (SD 0.3°), −1.3° (SD 0.3°), and 1.4° (SD 0.1°). Asian populations had the largest age-dependent change in LL, SS, and PT compared to any other ethnicity per decade at: −1.3° (SD 0.3°) to −0.5° (SD 1.3°), –1.2° (SD 0.2°) to −0.3° (SD 0.3°), and 1.7° (SD 0.2°) versus 1.1° (SD 0.1°), respectively. Conclusion. Ageing alters the orientation between the spine and pelvis, causing LL, SS, and PT to modify their orientations in a compensatory mechanism to maintain sagittal alignment for balance when standing. Asian populations have the largest degree of age-dependent change to their spinopelvic parameters compared to any other ethnicity, likely due to their lower PI. Cite this article: Bone Joint Res 2023;12(4):231–244

Aims. Pain is the most frequent complaint associated with osteonecrosis of the femoral head (ONFH), but the factors contributing to such pain are poorly understood. This study explored diverse demographic, clinical, radiological, psychological, and neurophysiological factors for their potential contribution to pain in patients with ONFH. Methods. This cross-sectional study was carried out according to the “STrengthening the Reporting of OBservational studies in Epidemiology” statement. Data on 19 variables were collected at a single timepoint from 250 patients with ONFH who were treated at our medical centre between July and December 2023 using validated instruments or, in the case of hip pain, a numerical rating scale. Factors associated with pain severity were identified using hierarchical multifactor linear regression. Results. Regression identified the following characteristics as independently associated with higher pain score, after adjustment for potential confounders: Association Research Circulation Osseous classification stage IIIa or IIIb, bone marrow oedema, grade 3 joint effusion, as well as higher scores on pain catastrophizing, anxiety, and central sensitization. The final model explained 69.7% of observed variance in pain scores, of which clinical and radiological factors explained 37%, while psychological and neurophysiological factors explained 24% and demographic factors explained 8.7%. Conclusion. Multidimensional characteristics jointly contribute to the severity of pain associated with ONFH. These findings highlight the need to comprehensively identify potential contributors to pain, and to personalize management and treatment accordingly. Cite this article: Bone Joint Res 2024;13(11):673–681

Objectives. Mesenchymal stem cells (MSCs) are of growing interest in terms of bone regeneration. Most preclinical trials utilize bone-marrow-derived mesenchymal stem cells (bMSCs), although this is not without isolation and expansion difficulties. The aim of this study was: to compare the characteristics of bMSCs and adipose-derived mesenchymal stem cells (AdMSCs) from juvenile, adult, and ovarectomized (OVX) rats; and to assess the effect of human parathyroid hormone (hPTH) 1-34 on their osteogenic potential and migration to stromal cell-derived factor-1 (SDF-1). Methods. Cells were isolated from the adipose and bone marrow of juvenile, adult, and previously OVX Wistar rats, and were characterized with flow cytometry, proliferation assays, osteogenic and adipogenic differentiation, and migration to SDF-1. Experiments were repeated with and without intermittent hPTH 1-34. Results. Juvenile and adult MSCs demonstrated significantly increased osteogenic and adipogenic differentiation and superior migration towards SDF-1 compared with OVX groups; this was the case for AdMSCs and bMSCs equally. Parathyroid hormone (PTH) increased parameters of osteogenic differentiation and migration to SDF-1. This was significant for all cell types, although it had the most significant effect on cells derived from OVX animals. bMSCs from all groups showed increased mineralization and migration to SDF-1 compared with AdMSCs. Conclusion. Juvenile MSCs showed significantly greater migration to SDF-1 and significantly greater osteogenic and adipogenic differentiation compared with cells from osteopenic rats; this was true for bMSCs and AdMSCs. The addition of PTH increased these characteristics, with the most significant effect on cells derived from OVX animals, further illustrating possible clinical application of both PTH and MSCs in bone regenerative therapies. Cite this article:L. Osagie-Clouard, A. Sanghani-Kerai, M. Coathup, R. Meeson, T. Briggs, G. Blunn. The influence of parathyroid hormone 1-34 on the osteogenic characteristics of adipose- and bone-marrow-derived mesenchymal stem cells from juvenile and ovarectomized rats. Bone Joint Res 2019;8:397–404. DOI: 10.1302/2046-3758.88.BJR-2019-0018.R1

Aims

This study examined the relationship between obesity (OB) and osteoporosis (OP), aiming to identify shared genetic markers and molecular mechanisms to facilitate the development of therapies that target both conditions simultaneously.

Aims. In this study, we aimed to visualize the spatial distribution characteristics of femoral head necrosis using a novel measurement method. Methods. We retrospectively collected CT imaging data of 108 hips with non-traumatic osteonecrosis of the femoral head from 76 consecutive patients (mean age 34.3 years (SD 8.1), 56.58% male (n = 43)) in two clinical centres. The femoral head was divided into 288 standard units (based on the orientation of units within the femoral head, designated as N[Superior], S[Inferior], E[Anterior], and W[Posterior]) using a new measurement system called the longitude and latitude division system (LLDS). A computer-aided design (CAD) measurement tool was also developed to visualize the measurement of the spatial location of necrotic lesions in CT images. Two orthopaedic surgeons independently performed measurements, and the results were used to draw 2D and 3D heat maps of spatial distribution of necrotic lesions in the femoral head, and for statistical analysis. Results. The results showed that the LLDS has high inter-rater reliability. As illustrated by the heat map, the distribution of Japanese Investigation Committee (JIC) classification type C necrotic lesions exhibited clustering characteristics, with the lesions being concentrated in the northern and eastern regions, forming a hot zone (90% probability) centred on the N4-N6E2, N3-N6E units of outer ring blocks. Statistical results showed that the distribution difference between type C2 and type C1 was most significant in the E1 and E2 units and, combined with the heat map, indicated that the spatial distribution differences at N3-N6E1 and N1-N3E2 units are crucial in understanding type C1 and C2 necrotic lesions. Conclusion. The LLDS can be used to accurately measure the spatial location of necrotic lesions and display their distribution characteristics. Cite this article: Bone Joint Res 2024;13(6):294–305

Aims. Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE. However, there is a noted gap in research on the rate and characteristics of subsequent SREs requiring further localized treatment, obligating clinicians to extrapolate from experiences with initial SREs when confronting subsequent ones. This study aimed to investigate the proportion of MBD patients developing subsequent SREs requiring local treatment, examine if there are prognostic differences at the initial treatment between those with single versus subsequent SREs, and determine if clinical, oncological, and prognostic features differ between initial and subsequent SRE treatments. Methods. This retrospective study included 3,814 adult patients who received local treatment – surgery and/or radiotherapy – for bone metastasis between 1 January 2010 and 31 December 2019. All included patients had at least one SRE requiring local treatment. A subsequent SRE was defined as a second SRE requiring local treatment. Clinical, oncological, and prognostic features were compared between single SREs and subsequent SREs using Mann-Whitney U test, Fisher’s exact test, and Kaplan–Meier curve. Results. Of the 3,814 patients with SREs, 3,159 (83%) patients had a single SRE and 655 (17%) patients developed a subsequent SRE. Patients who developed subsequent SREs generally had characteristics that favoured longer survival, such as higher BMI, higher albumin levels, fewer comorbidities, or lower neutrophil count. Once the patient got to the point of subsequent SRE, their clinical and oncological characteristics and one-year survival (28%) were not as good as those with only a single SRE (35%; p < 0.001), indicating that clinicians’ experiences when treating the initial SRE are not similar when treating a subsequent SRE. Conclusion. This study found that 17% of patients required treatments for a second, subsequent SRE, and the current clinical guideline did not provide a specific approach to this clinical condition. We observed that referencing the initial treatment, patients in the subsequent SRE group had longer six-week, 90-day, and one-year median survival than patients in the single SRE group. Once patients develop a subsequent SRE, they have a worse one-year survival rate than those who receive treatment for a single SRE. Future research should identify prognostic factors and assess the applicability of existing survival prediction models for better management of subsequent SREs. Cite this article: Bone Joint Res 2024;13(9):497–506

Aims. Knee osteoarthritis (OA) involves a variety of tissues in the joint. Gene expression profiles in different tissues are of great importance in order to understand OA. Methods. First, we obtained gene expression profiles of cartilage, synovium, subchondral bone, and meniscus from the Gene Expression Omnibus (GEO). Several datasets were standardized by merging and removing batch effects. Then, we used unsupervised clustering to divide OA into three subtypes. The gene ontology and pathway enrichment of three subtypes were analyzed. CIBERSORT was used to evaluate the infiltration of immune cells in different subtypes. Finally, OA-related genes were obtained from the Molecular Signatures Database for validation, and diagnostic markers were screened according to clinical characteristics. Quantitative reverse transcription polymerase chain reaction (qRT‐PCR) was used to verify the effectiveness of markers. Results. C1 subtype is mainly concentrated in the development of skeletal muscle organs, C2 lies in metabolic process and immune response, and C3 in pyroptosis and cell death process. Therefore, we divided OA into three subtypes: bone remodelling subtype (C1), immune metabolism subtype (C2), and cartilage degradation subtype (C3). The number of macrophage M0 and activated mast cells of C2 subtype was significantly higher than those of the other two subtypes. COL2A1 has significant differences in different subtypes. The expression of COL2A1 is related to age, and trafficking protein particle complex subunit 2 is related to the sex of OA patients. Conclusion. This study linked different tissues with gene expression profiles, revealing different molecular subtypes of patients with knee OA. The relationship between clinical characteristics and OA-related genes was also studied, which provides a new concept for the diagnosis and treatment of OA. Cite this article: Bone Joint Res 2023;12(12):702–711

Aims. To determine the major risk factors for unplanned reoperations (UROs) following corrective surgery for adult spinal deformity (ASD) and their interactions, using machine learning-based prediction algorithms and game theory. Methods. Patients who underwent surgery for ASD, with a minimum of two-year follow-up, were retrospectively reviewed. In total, 210 patients were included and randomly allocated into training (70% of the sample size) and test (the remaining 30%) sets to develop the machine learning algorithm. Risk factors were included in the analysis, along with clinical characteristics and parameters acquired through diagnostic radiology. Results. Overall, 152 patients without and 58 with a history of surgical revision following surgery for ASD were observed; the mean age was 68.9 years (SD 8.7) and 66.9 years (SD 6.6), respectively. On implementing a random forest model, the classification of URO events resulted in a balanced accuracy of 86.8%. Among machine learning-extracted risk factors, URO, proximal junction failure (PJF), and postoperative distance from the posterosuperior corner of C7 and the vertical axis from the centroid of C2 (SVA) were significant upon Kaplan-Meier survival analysis. Conclusion. The major risk factors for URO following surgery for ASD, i.e. postoperative SVA and PJF, and their interactions were identified using a machine learning algorithm and game theory. Clinical benefits will depend on patient risk profiles. Cite this article: Bone Joint Res 2023;12(4):245–255

Aims. This study aimed to determine the expression and clinical significance of a cartilage protein, cartilage oligomeric matrix protein (COMP), in knee osteoarthritis (OA) patients. Methods. A total of 270 knee OA patients and 93 healthy controls were recruited. COMP messenger RNA (mRNA) and protein levels in serum, synovial fluid, synovial tissue, and fibroblast-like synoviocytes (FLSs) of knee OA patients were determined using enzyme-linked immunosorbent assay, real-time polymerase chain reaction, and immunohistochemistry. Results. COMP protein levels were significantly elevated in serum and synovial fluid of knee OA patients, especially those in the advanced stages of the disease. Serum COMP was significantly correlated with radiological severity as well as measures of body composition, physical performance, knee pain, and disability. Receiver operating characteristic curve analysis unveiled a diagnostic value of serum COMP as a biomarker of knee OA (41.64 ng/ml, area under the curve (AUC) = 1.00), with a sensitivity of 99.6% and a specificity of 100.0%. Further analysis uncovered that COMP mRNA expression was markedly upregulated in the inflamed synovium of knee OA, consistent with immunohistochemical staining revealing localization of COMP protein in the lining and sub-lining layers of knee OA inflamed synovium. Most notably, relative COMP mRNA expression in knee OA synovium was positively associated with its protein levels in serum and synovial fluid of knee OA patients. In human knee OA FLSs activated with tumour necrosis factor-alpha, COMP mRNA expression was considerably up-regulated in a time-dependent manner. Conclusion. All results indicate that COMP might serve as a supportive diagnostic marker for knee OA in conjunction with the standard diagnostic methods. Cite this article: Bone Joint Res 2024;13(6):261–271

Aims. An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise. Methods. A smartwatch was used to collect inertial data from 42 patients performing shoulder physiotherapy exercises for rotator cuff injuries in both in-clinic and at-home settings. A two-stage ML approach was used to detect out-of-distribution (OOD) data (to remove non-exercise data) and subsequently for classification of exercises. We evaluated the performance impact of grouping exercises by motion type, inclusion of non-exercise data for algorithm training, and a patient-specific approach to exercise classification. Algorithm performance was evaluated using both in-clinic and at-home data. Results. The patient-specific approach with engineered features achieved the highest in-clinic performance for differentiating physiotherapy exercise from non-exercise activity (area under the receiver operating characteristic (AUROC) = 0.924). Including non-exercise data in algorithm training further improved classifier performance (random forest, AUROC = 0.985). The highest accuracy achieved for classifying individual in-clinic exercises was 0.903, using a patient-specific method with deep neural network model extracted features. Grouping exercises by motion type improved exercise classification. For at-home data, OOD detection yielded similar performance with the non-exercise data in the algorithm training (fully convolutional network AUROC = 0.919). Conclusion. Including non-exercise data in algorithm training improves detection of exercises. A patient-specific approach leveraging data from earlier patient-supervised sessions should be considered but is highly dependent on per-patient data quality. Cite this article: Bone Joint Res 2023;12(3):165–177

Aims. To investigate the optimal thresholds and diagnostic efficacy of commonly used serological and synovial fluid detection indexes for diagnosing periprosthetic joint infection (PJI) in patients who have rheumatoid arthritis (RA). Methods. The data from 348 patients who had RA or osteoarthritis (OA) and had previously undergone a total knee (TKA) and/or a total hip arthroplasty (THA) (including RA-PJI: 60 cases, RA-non-PJI: 80 cases; OA-PJI: 104 cases, OA-non-PJI: 104 cases) were retrospectively analyzed. A receiver operating characteristic curve was used to determine the optimal thresholds of the CRP, ESR, synovial fluid white blood cell count (WBC), and polymorphonuclear neutrophil percentage (PMN%) for diagnosing RA-PJI and OA-PJI. The diagnostic efficacy was evaluated by comparing the area under the curve (AUC) of each index and applying the results of the combined index diagnostic test. Results. For PJI prediction, the results of serological and synovial fluid indexes were different between the RA-PJI and OA-PJI groups. The optimal cutoff value of CRP for diagnosing RA-PJI was 12.5 mg/l, ESR was 39 mm/hour, synovial fluid WBC was 3,654/μl, and PMN% was 65.9%; and those of OA-PJI were 8.2 mg/l, 31 mm/hour, 2,673/μl, and 62.0%, respectively. In the RA-PJI group, the specificity (94.4%), positive predictive value (97.1%), and AUC (0.916) of synovial fluid WBC were higher than those of the other indexes. The optimal cutoff values of synovial fluid WBC and PMN% for diagnosing RA-PJI after THA were significantly higher than those of TKA. The specificity and positive predictive value of the combined index were 100%. Conclusion. Serum inflammatory and synovial fluid indexes can be used for diagnosing RA-PJI, for which synovial fluid WBC is the best detection index. Combining multiple detection indexes can provide a reference basis for the early and accurate diagnosis of RA-PJI. Cite this article: Bone Joint Res 2023;12(9):559–570

Aims. This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. Methods. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate. Results. Time series clustering allowed us to divide the patients into two groups, and the predictive factors were identified including patient- and operation-related factors. The area under the receiver operating characteristic (ROC) curve (AUC) for the BMD loss prediction averaged 0.734. Virtual administration of bisphosphonate showed on average 14% efficacy in preventing BMD loss of zone 7. Additionally, stem types and preoperative triglyceride (TG), creatinine (Cr), estimated glomerular filtration rate (eGFR), and creatine kinase (CK) showed significant association with the estimated patient-specific efficacy of bisphosphonate. Conclusion. Periprosthetic BMD loss after THA is predictable based on patient- and operation-related factors, and optimal prescription of bisphosphonate based on the prediction may prevent BMD loss. Cite this article: Bone Joint Res 2024;13(4):184–192

Aims. This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Methods. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization. Results. A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and external validation cohort (receiver operating characteristic > 0.8). The upregulated expression of these hub genes in osteoarthritic cartilage signified higher levels of immune infiltration as well as the expression of metalloproteinases and mineralization markers, suggesting harmful biological alterations and indicating that these hub genes play an important role in the pathogenesis of OA. A competing endogenous RNA network was constructed to reveal the underlying post-transcriptional regulatory mechanisms. Conclusion. The current study explores and validates a dysregulated key gene set in osteoarthritic cartilage that is capable of accurately diagnosing OA and characterizing the biological alterations in osteoarthritic cartilage; this may become a promising indicator in clinical decision-making. This study indicates that dysregulated key genes play an important role in the development and progression of OA, and may be potential therapeutic targets. Cite this article: Bone Joint Res 2024;13(2):66–82

Aims. This study aimed to explore the diagnostic value of synovial fluid neutrophil extracellular traps (SF-NETs) in periprosthetic joint infection (PJI) diagnosis, and compare it with that of microbial culture, serum ESR and CRP, synovial white blood cell (WBC) count, and polymorphonuclear neutrophil percentage (PMN%). Methods. In a single health centre, patients with suspected PJI were enrolled from January 2013 to December 2021. The inclusion criteria were: 1) patients who were suspected to have PJI; 2) patients with complete medical records; and 3) patients from whom sufficient synovial fluid was obtained for microbial culture and NET test. Patients who received revision surgeries due to aseptic failure (AF) were selected as controls. Synovial fluid was collected for microbial culture and SF-WBC, SF-PNM%, and SF-NET detection. The receiver operating characteristic curve (ROC) of synovial NET, WBC, PMN%, and area under the curve (AUC) were obtained; the diagnostic efficacies of these diagnostic indexes were calculated and compared. Results. The levels of SF-NETs in the PJI group were significantly higher than those of the AF group. The AUC of SF-NET was 0.971 (95% confidence interval (CI) 0.903 to 0.996), the sensitivity was 93.48% (95% CI 82.10% to 98.63%), the specificity was 96.43% (95% CI 81.65% to 99.91%), the accuracy was 94.60% (95% CI 86.73% to 98.50%), the positive predictive value was 97.73%, and the negative predictive value was 90%. Further analysis showed that SF-NET could improve the diagnosis of culture-negative PJI, patients with PJI who received antibiotic treatment preoperatively, and fungal PJI. Conclusion. SF-NET is a novel and ideal synovial fluid biomarker for PJI diagnosis, which could improve PJI diagnosis greatly. Cite this article: Bone Joint Res 2023;12(2):113–120

Aims. Highly cross-linked polyethylene (HXLPE) greatly reduces wear in total hip arthroplasty, compared to conventional polyethylene (CPE). Cross-linking is commonly achieved by irradiation. This study aimed to compare the degree of cross-linking and in vitro wear rates across a cohort of retrieved and unused polyethylene cups/liners from various brands. Methods. Polyethylene acetabular cups/liners were collected at one centre from 1 April 2021 to 30 April 2022. The trans-vinylene index (TVI) and oxidation index (OI) were determined by Fourier-transform infrared spectrometry. Wear was measured using a pin-on-disk test. Results. A total of 47 specimens from ten brands were included. The TVI was independent of time in vivo. A linear correlation (R. 2. = 0.995) was observed between the old and current TVI standards, except for vitamin E-containing polyethylene. The absorbed irradiation dose calculated from the TVI corresponded to product specifications for all but two products. For one electron beam-irradiated HXLPE, a mean dose of 241% (SD 18%) of specifications was determined. For another, gamma-irradiated HXLPE, a mean 41% (SD 13%) of specifications was determined. Lower wear was observed for higher TVI. Conclusion. The TVI is a reliable measure of the absorbed irradiation dose and does not alter over time in vivo. The products of various brands differ by manufacturing details and consequently cross-linking characteristics. Absorption and penetration of electron radiation and gamma radiation differ, potentially leading to higher degrees of cross-linking for electron radiation. There is a non-linear, inverse correlation between TVI and in vitro wear. The wear resistance of the HXLPE with low TVI was reduced and more comparable to CPE. Cite this article: Bone Joint Res 2024;13(11):682–693

Aims. This study was designed to characterize the recurrence incidence and risk factors of antibiotic-loaded cement spacer (ALCS) for definitive bone defect treatment in limb osteomyelitis. Methods. We included adult patients with limb osteomyelitis who received debridement and ALCS insertion into the bone defect as definitive management between 2013 and 2020 in our clinical centre. The follow-up time was at least two years. Data on patients’ demographics, clinical characteristics, and infection recurrence were retrospectively collected and analyzed. Results. In total, 314 patients with a mean age of 52.1 years (SD 12.1) were enrolled. After a mean of 50 months’ (24 to 96) follow-up, 53 (16.9%) patients had infection recurrence including 32 tibiae, ten femora, ten calcanea, and one humerus. Of all patients with recurrence, 30 (9.6%) occurred within one year and 39 (12.4%) within two years. Among them, 41 patients needed reoperation, five received antibiotics treatment only, and seven ultimately required amputations. Following multivariable analysis, we found that patients infected with Gram-negative bacilli were more likely to have a recurrence (odds ratio (OR) 2.38, 95% confidence interval (CI) 1.20 to 6.94; p = 0.046) compared to Staphylococcus aureus; segmental bone defects (OR 5.25, 95% CI 1.80 to 15.26; p = 0.002) and smoking (OR 3.00, 95% CI 1.39 to 6.50; p = 0.005) were also independent risk factors for recurrence after treatment. Conclusion. Permanent ALCS might be an alternative strategy for definitive bone defect management in selected osteomyelitis cases. However, the overall high recurrence found suggests that it should be cautiously treated. Additionally, segmental defects, Gram-negative infections, and smoking were associated with an increased risk of infection recurrence. Cite this article: Bone Joint Res 2023;12(8):467–475

Aims. This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images. Methods. The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm. 3. ). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis. Results. CT-aBMD was successfully measured in 976/978 hips (99.8%). A significant correlation was found between CT-aBMD and DXA-BMD (r = 0.941; p < 0.001). In the ROC analysis, the area under the curve to diagnose osteoporosis was 0.976. The diagnostic sensitivity and specificity were 88.9% and 96%, respectively, with the cutoff set at 0.625 g/cm. 2. . Conclusion. Accurate DXA-BMD measurements and diagnosis of osteoporosis were performed from CT images using the system developed herein. As the models are open-source, clinicians can use the proposed system to screen osteoporosis and determine the surgical strategy for hip surgery. Cite this article: Bone Joint Res 2023;12(9):590–597

Aims. To explore key stakeholder views around feasibility and acceptability of trials seeking to prevent post-traumatic osteoarthritis (PTOA) following knee injury, and provide guidance for next steps in PTOA trial design. Methods. Healthcare professionals, clinicians, and/or researchers (HCP/Rs) were surveyed, and the data were presented at a congress workshop. A second and related survey was then developed for people with joint damage caused by knee injury and/or osteoarthritis (PJDs), who were approached by a UK Charity newsletter or Oxford involvement registry. Anonymized data were collected and analyzed in Qualtrics. Results. Survey responses (n = 19 HCP/Rs, 39 PJDs) supported studies testing pharmacological agents preventing PTOA. All HCP/Rs and 30/31 (97%) PJDs supported the development of new treatments that improved or delayed knee symptoms and damage to knee structure. PJDs thought that improving structural knee damage was more important than knee symptoms. Both groups found studies more acceptable as expected future benefit and risk of PTOA increased. All drug delivery routes were acceptable. Workshop participants (around n = 60) reflected survey views. Discussions suggested that stratifying using molecular testing for likely drug response appeared to be more acceptable than using characteristics such as sex, age, and BMI. Conclusion. Our findings supported PTOA drug intervention studies, including situations where there is low risk of disease, no expected benefit of treatment, and frequent treatment administration. PJDs appeared less risk-averse than HCP/Rs. This work reinforces the benefits of consensus and involvement work in the co-creation of PTOA drug trial design. Involvement of key stakeholders, such as PJDs with different risks of OA and regulatory representatives, are critical for trial design success. Cite this article: Bone Joint Res 2024;13(9):513–524

Aims. Serum inflammatory parameters are widely used to aid in diagnosing a periprosthetic joint infection (PJI). Due to their limited performances in the literature, novel and more accurate biomarkers are needed. Serum albumin-to-globulin ratio (AGR) and serum CRP-to-albumin ratio (CAR) have previously been proposed as potential new parameters, but results were mixed. The aim of this study was to assess the diagnostic accuracy of AGR and CAR in diagnosing PJI and to compare them to the established and widely used marker CRP. Methods. From 2015 to 2022, a consecutive series of 275 cases of revision total hip (n = 129) and knee arthroplasty (n = 146) were included in this retrospective cohort study. Based on the 2021 European Bone and Joint Infection Society (EBJIS) definition, 144 arthroplasties were classified as septic. Using receiver operating characteristic curve (ROC) analysis, the ideal thresholds and diagnostic performances were calculated. The areas under the curve (AUCs) were compared using the z-test. Results. AGR, CAR, and CRP were associated with PJI (p < 0.001). Sensitivities were 62.5% (95% CI 54.3 to 70.0), 73.6% (95% CI 65.8 to 80.1), and 71.5% (95% CI 63.6 to 78.3), respectively. Specificities were calculated with 84.7% (95% CI 77.5 to 89.9), 86.3% (95% CI 79.2 to 91.2), and 87.8% (95% CI 80.9 to 92.4), respectively. The AUC of CRP (0.797 (95% CI 0.750 to 0.843)) was significantly higher than the AUC of AGR (0.736 (95% CI 0.686 to 0.786), p < 0.001), and similar to AUC of CAR (0.799 (95% CI 0.753 to 0.846), p = 0.832). Decreased sensitivities were observed in PJIs caused by low-virulence organisms (AGR: 60%, CAR: 78%) compared to high-virulence pathogens (AGR: 80%, p = 0.042; CAR: 88%, p = 0.158). Higher sensitivities were seen in acute haematogenous (AGR: 83%, CAR: 96%) compared to chronic PJIs (AGR: 54%, p = 0.001; CAR: 65%, p < 0.001). Conclusion. Serum AGR and CAR showed limited diagnostic accuracy (especially in low-grade and chronic infections) and did not outperform the established marker CRP in our study. Hence, neither parameter can be recommended as an additional tool for diagnosing PJI. Cite this article: Bone Joint Res 2024;13(8):372–382

Aims. We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. Methods. Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell’s concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature. Results. We identified three PMOP-related subtypes and four core modules. The muscle system process, muscle contraction, and actin filament-based movement were more active in the hub genes. We obtained five feature genes related to PMOP. Our analysis verified that the gene signature had good predictive power and applicability. The outcomes of the GSE56815 cohort were found to be consistent with the results of the earlier studies. qRT-PCR results showed that RAB2A and FYCO1 were amplified in clinical samples. Conclusion. The PMOP-related gene signature we developed and verified can accurately predict the risk of PMOP in patients. These results can elucidate the molecular mechanism of RAB2A and FYCO1 underlying PMOP, and yield new and improved treatment strategies, ultimately helping PMOP monitoring. Cite this article: Bone Joint Res 2022;11(8):548–560