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. Our study included 226 nonoperatively treated humeral shaft fractures. We evaluated the interobserver reliability and intraobserver reproducibility of RUSHU scoring using intraclass correlation coefficients (ICCs). Additionally, we determined the optimal cut-off thresholds for predicting nonunion using the receiver operating characteristic (ROC) method.Aims
Methods
Developmental cervical spinal stenosis (DcSS) is a well-known predisposing factor for degenerative cervical myelopathy (DCM) but there is a lack of consensus on its definition. This study aims to define DcSS based on MRI, and its multilevel characteristics, to assess the prevalence of DcSS in the general population, and to evaluate the presence of DcSS in the prediction of developing DCM. This cross-sectional study analyzed MRI spine morphological parameters at C3 to C7 (including anteroposterior (AP) diameter of spinal canal, spinal cord, and vertebral body) from DCM patients (n = 95) and individuals recruited from the general population (n = 2,019). Level-specific median AP spinal canal diameter from DCM patients was used to screen for stenotic levels in the population-based cohort. An individual with multilevel (≥ 3 vertebral levels) AP canal diameter smaller than the DCM median values was considered as having DcSS. The most optimal cut-off canal diameter per level for DcSS was determined by receiver operating characteristic analyses, and multivariable logistic regression was performed for the prediction of developing DCM that required surgery.Aims
Methods
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. 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.Aims
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Treatment of high-grade limb bone sarcoma that invades a joint requires en bloc extra-articular excision. MRI can demonstrate joint invasion but is frequently inconclusive, and its predictive value is unknown. We evaluated the diagnostic accuracy of direct and indirect radiological signs of intra-articular tumour extension and the performance characteristics of MRI findings of intra-articular tumour extension. We performed a retrospective case-control study of patients who underwent extra-articular excision for sarcoma of the knee, hip, or shoulder from 1 June 2000 to 1 November 2020. Radiologists blinded to the pathology results evaluated preoperative MRI for three direct signs of joint invasion (capsular disruption, cortical breach, cartilage invasion) and indirect signs (e.g. joint effusion, synovial thickening). The discriminatory ability of MRI to detect intra-articular tumour extension was determined by receiver operating characteristic analysis.Aims
Methods
Aims. The
The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support. The dataset consisted of 26,121 anonymized anterior-posterior (AP) and lateral standard view radiographs of the wrist, with and without DRF. The convolutional neural network (CNN) model was trained to detect the presence of a DRF by comparing the radiographs containing a fracture to the inconspicuous ones. A total of 11 physicians (six surgeons in training and five hand surgeons) assessed 200 pairs of randomly selected digital radiographs of the wrist (AP and lateral) for the presence of a DRF. The same images were first evaluated without, and then with, the support of the CNN model, and the diagnostic accuracy of the two methods was compared.Aims
Methods
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. Using weighted gene co-expression network analysis (WGCNA), we analyzed datasets from the Gene Expression Omnibus (GEO) database to identify co-expressed gene modules in OB and OP. These modules underwent Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction analysis to discover Hub genes. Machine learning refined the gene selection, with further validation using additional datasets. Single-cell analysis emphasized specific cell subpopulations, and enzyme-linked immunosorbent assay (ELISA), protein blotting, and cellular staining were used to investigate key genes.Aims
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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. 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.Aims
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Aims. This study aimed to evaluate the BioFire Joint Infection (JI) Panel in cases of hip and knee periprosthetic joint infection (PJI) where conventional microbiology is unclear, and to assess its role as a complementary intraoperative diagnostic tool. Methods. Five groups representing common microbiological scenarios in hip and knee revision arthroplasty were selected from our arthroplasty registry, prospectively maintained PJI databases, and biobank: 1) unexpected-negative cultures (UNCs), 2) unexpected-positive cultures (UPCs), 3) single-positive intraoperative cultures (SPCs), and 4) clearly septic and 5) aseptic cases. In total, 268 archived synovial fluid samples from 195 patients who underwent acute/chronic revision total hip or knee arthroplasty were included. Cases were classified according to the International Consensus Meeting 2018 criteria. JI panel evaluation of synovial fluid was performed, and the results were compared with cultures. Results. The JI panel detected microorganisms in 7/48 (14.5%) and 15/67 (22.4%) cases related to UNCs and SPCs, respectively, but not in cases of UPCs. The correlation between JI panel detection and infection classification criteria for early/late acute and chronic PJI was 46.6%, 73%, and 40%, respectively. Overall, the JI panel identified 12.6% additional microorganisms and three new species. The JI panel pathogen identification showed a
To investigate if preoperative CT improves detection of unstable trochanteric hip fractures. A single-centre prospective study was conducted. Patients aged 65 years or older with trochanteric hip fractures admitted to Stavanger University Hospital (Stavanger, Norway) were consecutively included from September 2020 to January 2022. Radiographs and CT images of the fractures were obtained, and surgeons made individual assessments of the fractures based on these. The assessment was conducted according to a systematic protocol including three classification systems (AO/Orthopaedic Trauma Association (OTA), Evans Jensen (EVJ), and Nakano) and questions addressing specific fracture patterns. An expert group provided a gold-standard assessment based on the CT images. Sensitivities and specificities of surgeons’ assessments were estimated and compared in regression models with correlations for the same patients. Intra- and inter-rater reliability were presented as Cohen’s kappa and Gwet’s agreement coefficient (AC1).Aims
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This study aimed to determine the expression and clinical significance of a cartilage protein, cartilage oligomeric matrix protein (COMP), in knee osteoarthritis (OA) patients. 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.Aims
Methods
The primary objective of this registry-based study was to compare patient-reported outcomes of cementless and cemented medial unicompartmental knee arthroplasty (UKA) during the first postoperative year. The secondary objective was to assess one- and three-year implant survival of both fixation techniques. We analyzed 10,862 cementless and 7,917 cemented UKA cases enrolled in the Dutch Arthroplasty Registry, operated between 2017 and 2021. Pre- to postoperative change in outcomes at six and 12 months’ follow-up were compared using mixed model analyses. Kaplan-Meier and Cox regression models were applied to quantify differences in implant survival. Adjustments were made for patient-specific variables and annual hospital volume.Aims
Methods
Chondrosarcoma is the second most common surgically treated primary bone sarcoma. Despite a large number of scientific papers in the literature, there is still significant controversy about diagnostics, treatment of the primary tumour, subtypes, and complications. Therefore, consensus on its day-to-day treatment decisions is needed. In January 2024, the Birmingham Orthopaedic Oncology Meeting (BOOM) attempted to gain global consensus from 300 delegates from over 50 countries. The meeting focused on these critical areas and aimed to generate consensus statements based on evidence amalgamation and expert opinion from diverse geographical regions. In parallel, periprosthetic joint infection (PJI) in oncological reconstructions poses unique challenges due to factors such as adjuvant treatments, large exposures, and the complexity of surgery. The meeting debated two-stage revisions, antibiotic prophylaxis, managing acute PJI in patients undergoing chemotherapy, and defining the best strategies for wound management and allograft reconstruction. The objectives of the meeting extended beyond resolving immediate controversies. It sought to foster global collaboration among specialists attending the meeting, and to encourage future research projects to address unsolved dilemmas. By highlighting areas of disagreement and promoting collaborative research endeavours, this initiative aims to enhance treatment standards and potentially improve outcomes for patients globally. This paper sets out some of the controversies and questions that were debated in the meeting. Cite this article:
This systematic review aims to identify 3D predictors derived from biplanar reconstruction, and to describe current methods for improving curve prediction in patients with mild adolescent idiopathic scoliosis. A comprehensive search was conducted by three independent investigators on MEDLINE, PubMed, Web of Science, and Cochrane Library. Search terms included “adolescent idiopathic scoliosis”,“3D”, and “progression”. The inclusion and exclusion criteria were carefully defined to include clinical studies. Risk of bias was assessed with the Quality in Prognostic Studies tool (QUIPS) and Appraisal tool for Cross-Sectional Studies (AXIS), and level of evidence for each predictor was rated with the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. In all, 915 publications were identified, with 377 articles subjected to full-text screening; overall, 31 articles were included.Aims
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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. 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.Aims
Methods
This study aimed to evaluate the clinical application of the PJI-TNM classification for periprosthetic joint infection (PJI) by determining intraobserver and interobserver reliability. To facilitate its use in clinical practice, an educational app was subsequently developed and evaluated. A total of ten orthopaedic surgeons classified 20 cases of PJI based on the PJI-TNM classification. Subsequently, the classification was re-evaluated using the PJI-TNM app. Classification accuracy was calculated separately for each subcategory (reinfection, tissue and implant condition, non-human cells, and morbidity of the patient). Fleiss’ kappa and Cohen’s kappa were calculated for interobserver and intraobserver reliability, respectively.Aims
Methods
To evaluate mid-to long-term patient-reported outcome measures (PROMs) of endoprosthetic reconstruction after resection of malignant tumours arising around the knee, and to investigate the risk factors for unfavourable PROMs. The medical records of 75 patients who underwent surgery between 2000 and 2020 were retrospectively reviewed, and 44 patients who were alive and available for follow-up (at a mean of 9.7 years postoperatively) were included in the study. Leg length discrepancy was measured on whole-leg radiographs, and functional assessment was performed with PROMs (Toronto Extremity Salvage Score (TESS) and Comprehensive Outcome Measure for Musculoskeletal Oncology Lower Extremity (COMMON-LE)) with two different aspects. The thresholds for unfavourable PROMs were determined using anchor questions regarding satisfaction, and the risk factors for unfavourable PROMs were investigated.Aims
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We aim to evaluate the usefulness of postoperative blood tests by investigating the incidence of abnormal results following total joint replacement (TJR), as well as identifying preoperative risk factors for abnormal blood test results postoperatively, especially pertaining to anaemia and acute kidney injury (AKI). This is a retrospective cohort study of patients who had elective TJR between January and December 2019 at a tertiary centre. Data gathered included age at time of surgery, sex, BMI, American Society of Anesthesiologists (ASA) grade, preoperative and postoperative laboratory test results, haemoglobin (Hgb), white blood count (WBC), haematocrit (Hct), platelets (Plts), sodium (Na+), potassium (K+), creatinine (Cr), estimated glomerular filtration rate (eGFR), and Ferritin (ug/l). Abnormal blood tests, AKI, electrolyte imbalance, anaemia, transfusion, reoperation, and readmission within one year were reported.Aims
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Aims. The diagnosis of periprosthetic joint infection (PJI) can be challenging as the symptoms are similar to other conditions, and the markers used for diagnosis have limited
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
