Aims. The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Methods. Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA. Results. PLS-DA produced a streamlined biomarker model with excellent sensitivity (95%), specificity (98.4%), and reliability (97.4%). The eight-biomarker model produced a fingerprint for esOA comprising type IIA procollagen N-terminal propeptide (PIIANP), tissue inhibitor of metalloproteinase (TIMP)-1, a disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS-4), monocyte chemoattractant protein (MCP)-1, interferon-γ-inducible protein-10 (IP-10), and transforming growth factor (TGF)-β3. Receiver operating characteristic (ROC) analysis demonstrated excellent discriminatory accuracy: area under the curve (AUC) being 0.970 for esOA, 0.957 for knee injury, and 1 for inflammatory arthritis. All ten validation test patients were classified correctly as esOA (accuracy 100%; reliability 100%) by the biomarker model. Conclusion. SF analysis coupled with machine learning produced a partially validated biomarker model with cohort-specific fingerprints that accurately and reliably discriminated esOA from knee injury and inflammatory arthritis with almost 100% efficacy. The presented findings and approach represent a new biomarker concept and potential diagnostic tool to stage disease in therapy trials and monitor the efficacy of such interventions. Cite this article: Bone Joint Res 2020;9(9):623–632

Aims. The diagnosis of joint infections is an inexact science using combinations of blood inflammatory markers and microscopy, culture, and sensitivity of synovial fluid (SF). There is potential for small molecule metabolites in infected SF to act as infection markers that could improve accuracy and speed of detection. The objective of this study was to use nuclear magnetic resonance (NMR) spectroscopy to identify small molecule differences between infected and noninfected human SF. Methods. In all, 16 SF samples (eight infected native and prosthetic joints plus eight noninfected joints requiring arthroplasty for end-stage osteoarthritis) were collected from patients. NMR spectroscopy was used to analyze the metabolites present in each sample. Principal component analysis and univariate statistical analysis were undertaken to investigate metabolic differences between the two groups. Results. A total of 16 metabolites were found in significantly different concentrations between the groups. Three were in higher relative concentrations (lipids, cholesterol, and N-acetylated molecules) and 13 in lower relative concentrations in the infected group (citrate, glycine, glycosaminoglycans, creatinine, histidine, lysine, formate, glucose, proline, valine, dimethylsulfone, mannose, and glutamine). Conclusion. Metabolites found in significantly greater concentrations in the infected cohort are markers of inflammation and infection. They play a role in lipid metabolism and the inflammatory response. Those found in significantly reduced concentrations were involved in carbohydrate metabolism, nucleoside metabolism, the glutamate metabolic pathway, increased oxidative stress in the diseased state, and reduced articular cartilage breakdown. This is the first study to demonstrate differences in the metabolic profile of infected and noninfected human SF, using a noninfected matched cohort, and may represent putative biomarkers that form the basis of new diagnostic tests for infected SF. Cite this article: Bone Joint Res 2021;10(1):85–95

Aims. The Wrightington classification system of fracture-dislocations of the elbow divides these injuries into six subtypes depending on the involvement of the coronoid and the radial head. The aim of this study was to assess the reliability and reproducibility of this classification system. Methods. This was a blinded study using radiographs and CT scans of 48 consecutive patients managed according to the Wrightington classification system between 2010 and 2018. Four trauma and orthopaedic consultants, two post CCT fellows, and one speciality registrar based in the UK classified the injuries. The seven observers reviewed preoperative radiographs and CT scans twice, with a minimum four-week interval. Radiographs and CT scans were reviewed separately. Inter- and intraobserver reliability were calculated using Fleiss and Cohen kappa coefficients. The Landis and Koch criteria were used to interpret the strength of the kappa values. Validity was assessed by calculating the percentage agreement against intraoperative findings. Results. Of the 48 patients, three (6%) had type A injury, 11 (23%) type B, 16 (33%) type B+, 16 (33%) Type C, two (4%) type D+, and none had a type D injury. All 48 patients had anteroposterior (AP) and lateral radiographs, 44 had 2D CT scans, and 39 had 3D reconstructions. The interobserver reliability kappa value was 0.52 for radiographs, 0.71 for 2D CT scans, and 0.73 for a combination of 2D and 3D reconstruction CT scans. The median intraobserver reliability was 0.75 (interquartile range (IQR) 0.62 to 0.79) for radiographs, 0.77 (IQR 0.73 to 0.94) for 2D CT scans, and 0.89 (IQR 0.77 to 0.93) for the combination of 2D and 3D reconstruction. Validity analysis showed that accuracy significantly improved when using CT scans (p = 0.018 and p = 0.028 respectively). Conclusion. The Wrightington classification system is a reliable and valid method of classifying fracture-dislocations of the elbow. CT scans are significantly more accurate than radiographs when identifying the pattern of injury, with good intra- and interobserver reproducibility. Cite this article: Bone Joint J 2020;102-B(8):1041–1047

Aims. Preoperative diagnosis is important for revision surgery after prosthetic joint infection (PJI). The purpose of our study was to determine whether reverse transcription-quantitative polymerase chain reaction (RT-qPCR), which is used to detect bacterial ribosomal RNA (rRNA) preoperatively, can reveal PJI in low volumes of aspirated fluid. Methods. We acquired joint fluid samples (JFSs) by preoperative aspiration from patients who were suspected of having a PJI and failed arthroplasty; patients with preoperative JFS volumes less than 5 ml were enrolled. RNA-based polymerase chain reaction (PCR) and bacterial culture were performed, and diagnostic efficiency was compared between the two methods.According to established Musculoskeletal Infection Society (MSIS) criteria, 21 of the 33 included patients were diagnosed with PJI. Results. RNA-based PCR exhibited 57.1% sensitivity, 91.7% specificity, 69.7% accuracy, 92.3% positive predictive value, and 55.0% negative predictive value. The corresponding values for culture were 28.6%, 83.3%, 48.5%, 75.0%, and 40.0%, respectively. A significantly higher sensitivity was thus obtained with the PCR method versus the culture method. Conclusion. In situations in which only a small JFS volume can be acquired, RNA-based PCR analysis increases the utility of preoperative puncture for patients who require revision surgery due to suspected PJI. Cite this article:Bone Joint Res. 2020;9(5):219–224

Aims. Biopsy of the periprosthetic tissue is an important diagnostic tool for prosthetic joint infection (PJI) as it enables the detection of the responsible microorganism with its sensitivity to antibiotics. We aimed to investigate how often the bacteria identified in the tissue analysis differed between samples obtained from preoperative biopsy and intraoperative revision surgery in cases of late PJI; and whether there was a therapeutic consequence. Methods. A total of 508 patients who required revision surgery of total hip arthroplasty (THA) (n = 231) or total knee arthroplasty (TKA) (n = 277) because of component loosening underwent biopsy before revision surgery. The tissue samples collected at biopsy and during revision surgery were analyzed according to the criteria of the Musculoskeletal Infection Society (MSIS). Results. In total, 178 (113 THA, 65 TKA) were classified as infected. The biopsy procedure had a sensitivity of 93.8%, a specificity of 97.3%, a positive predictive value (PPV) of 94.9%, a negative predictive value (NPV) of 96.7%, and an accuracy of 96.1%. Of the 178 infected patients, 26 showed a difference in the detected bacteria from the biopsy and the revision surgery (14.6%). This difference required a change to antibiotic therapy in only two cases (1.1%). Conclusion. Biopsy is a useful tool to diagnose PJI, but there may be a difference in the detected bacteria between the biopsy and revision surgery. However, this did not affect the choice of antibiotic therapy in most cases, rendering the clinical relevance of this phenomenon as low. Cite this article: Bone Joint J 2020;102-B(3):329–335

Aims. Failure of irrigation and debridement (I&D) for prosthetic joint infection (PJI) is influenced by numerous host, surgical, and pathogen-related factors. We aimed to develop and validate a practical, easy-to-use tool based on machine learning that may accurately predict outcome following I&D surgery taking into account the influence of numerous factors. Methods. This was an international, multicentre retrospective study of 1,174 revision total hip (THA) and knee arthroplasties (TKA) undergoing I&D for PJI between January 2005 and December 2017. PJI was defined using the Musculoskeletal Infection Society (MSIS) criteria. A total of 52 variables including demographics, comorbidities, and clinical and laboratory findings were evaluated using random forest machine learning analysis. The algorithm was then verified through cross-validation. Results. Of the 1,174 patients that were included in the study, 405 patients (34.5%) failed treatment. Using random forest analysis, an algorithm that provides the probability for failure for each specific patient was created. By order of importance, the ten most important variables associated with failure of I&D were serum CRP levels, positive blood cultures, indication for index arthroplasty other than osteoarthritis, not exchanging the modular components, use of immunosuppressive medication, late acute (haematogenous) infections, methicillin-resistant Staphylococcus aureus infection, overlying skin infection, polymicrobial infection, and older age. The algorithm had good discriminatory capability (area under the curve = 0.74). Cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. Conclusion. This is the first study in the orthopaedic literature to use machine learning as a tool for predicting outcomes following I&D surgery. The developed algorithm provides the medical profession with a tool that can be employed in clinical decision-making and improve patient care. Future studies should aid in further validating this tool on additional cohorts. Cite this article: Bone Joint J 2020;102-B(7 Supple B):11–19

Aims. The mobile bearing Oxford unicompartmental knee arthroplasty (OUKA) is recommended to be performed with the leg in the hanging leg (HL) position, and the thigh placed in a stirrup. This comparative cadaveric study assesses implant positioning and intraoperative kinematics of OUKA implanted either in the HL position or in the supine leg (SL) position. Methods. A total of 16 fresh-frozen knees in eight human cadavers, without macroscopic anatomical defects, were selected. The knees from each cadaver were randomized to have the OUKA implanted in the HL or SL position. Results. Tibial base plate rotation was significantly more variable in the SL group with 75% of tibiae mal-rotated. Multivariate analysis of navigation data found no difference based on all kinematic parameters across the range of motion (ROM). However, area under the curve analysis showed that knees placed in the HL position had much smaller differences between the pre- and post-surgery conditions for kinematics mean values across the entire ROM. Conclusion. The sagittal tibia cut, not dependent on standard instrumentation, determines the tibial component rotation. The HL position improves accuracy of this step compared to the SL position, probably due to better visuospatial orientation of the hip and knee to the surgeon. The HL position is better for replicating native kinematics of the knee as shown by the area under the curve analysis. In the supine knee position, care must be taken during the sagittal tibia cut, while checking flexion balance and when sizing the tibial component

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Steroid injections are used for subacromial pain syndrome and can be administered via the anterolateral or posterior approach to the subacromial space. It is not currently known which approach is superior in terms of improving clinical symptoms and function. This is the protocol for a randomized controlled trial (RCT) to compare the clinical effectiveness of a steroid injection given via the anterolateral or the posterior approach to the subacromial space.

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Cite this article: Bone Joint Res 2023;12(7):447–454.

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The aim of this study was to evaluate the optimal deep tissue specimen sample number for histopathological analysis in the diagnosis of periprosthetic joint infection (PJI).

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Distal femoral resection in conventional total knee arthroplasty (TKA) utilizes an intramedullary guide to determine coronal alignment, commonly planned for 5° of valgus. However, a standard 5° resection angle may contribute to malalignment in patients with variability in the femoral anatomical and mechanical axis angle. The purpose of the study was to leverage deep learning (DL) to measure the femoral mechanical-anatomical axis angle (FMAA) in a heterogeneous cohort.

Advanced 3D imaging and CT-based navigation have emerged as valuable tools to use in total knee arthroplasty (TKA), for both preoperative planning and the intraoperative execution of different philosophies of alignment. Preoperative planning using CT-based 3D imaging enables more accurate prediction of the size of components, enhancing surgical workflow and optimizing the precision of the positioning of components. Surgeons can assess alignment, osteophytes, and arthritic changes better. These scans provide improved insights into the patellofemoral joint and facilitate tibial sizing and the evaluation of implant-bone contact area in cementless TKA. Preoperative CT imaging is also required for the development of patient-specific instrumentation cutting guides, aiming to reduce intraoperative blood loss and improve the surgical technique in complex cases. Intraoperative CT-based navigation and haptic guidance facilitates precise execution of the preoperative plan, aiming for optimal positioning of the components and accurate alignment, as determined by the surgeon’s philosophy. It also helps reduce iatrogenic injury to the periarticular soft-tissue structures with subsequent reduction in the local and systemic inflammatory response, enhancing early outcomes. Despite the increased costs and radiation exposure associated with CT-based navigation, these many benefits have facilitated the adoption of imaged based robotic surgery into routine practice. Further research on ultra-low-dose CT scans and exploration of the possible translation of the use of 3D imaging into improved clinical outcomes are required to justify its broader implementation.

Cite this article: Bone Joint J 2024;106-B(9):892–897.

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Proper preoperative planning benefits fracture reduction, fixation, and stability in tibial plateau fracture surgery. We developed and clinically implemented a novel workflow for 3D surgical planning including patient-specific drilling guides in tibial plateau fracture surgery.

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered.

Cite this article: Bone Joint J 2022;104-B(12):1292–1303.

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Ankle fracture fixation is commonly performed by junior trainees. Simulation training using cadavers may shorten the learning curve and result in a technically superior surgical performance.

The June 2024 Wrist & Hand Roundup³⁶⁰ looks at: One-year outcomes of the anatomical front and back reconstruction for scapholunate dissociation; Limited intercarpal fusion versus proximal row carpectomy in the treatment of SLAC or SNAC wrist: results after 3.5 years; Prognostic factors for clinical outcomes after arthroscopic treatment of traumatic central tears of the triangular fibrocartilage complex; The rate of nonunion in the MRI-detected occult scaphoid fracture: a multicentre cohort study; Does correction of carpal malalignment influence the union rate of scaphoid nonunion surgery?; Provision of a home-based video-assisted therapy programme in thumb carpometacarpal arthroplasty; Is replantation associated with better hand function after traumatic hand amputation than after revision amputation?; Diagnostic performance of artificial intelligence for detection of scaphoid and distal radius fractures: a systematic review.

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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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Unicompartmental knee arthroplasty (UKA) is the preferred treatment for anterior medial knee osteoarthritis (OA) owing to the rapid postoperative recovery. However, the risk factors for UKA failure remain controversial.

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Current levels of hip fracture morbidity contribute greatly to the overall burden on health and social care services. Given the anticipated ageing of the population over the coming decade, there is potential for this burden to increase further, although the exact scale of impact has not been identified in contemporary literature. We therefore set out to predict the future incidence of hip fracture and help inform appropriate service provision to maintain an adequate standard of care.