Aims. The aim of this study was to determine the diagnostic utility of histological analysis in spinal biopsies for spondylodiscitis (SD). Patients and Methods. Clinical features, radiology, results of microbiology, histology, and laboratory investigations in 50 suspected SD patients were evaluated. In 29 patients, the final (i.e. treatment-based) diagnosis was pyogenic SD; in seven patients, the final diagnosis was mycobacterial SD. In pyogenic SD, the neutrophil polymorph (NP) infiltrate was scored semi-quantitatively by determining the mean number of NPs per (×400) high-power field (HPF). Results. Of the 29 pyogenic SD patients, 17 had positive microbiology and 21 positive histology (i.e. one or more NPs per HPF on average). All non-SD patients showed less than one NP per HPF. The presence of one or more NPs per HPF had a diagnostic sensitivity of 72.4%, specificity 100%, accuracy 100%, positive predictive value (PPV) 81.0%, and negative predictive value (NPV) 61.9%. Sensitivity, specificity, and accuracy were greater using the criterion of positive histology and/or microbiology than positive histology or microbiology alone. Granulomas were identified histologically in seven mycobacterial SD patients, and positive microbiology was detected in four. Conclusion. The diagnosis of pyogenic SD was more often confirmed by positive histology (one or more NPs per HPF on average) than by microbiology, although diagnostic sensitivity was greater when both histology and microbiology were positive. Cite this article: Bone Joint J 2019;101-B:246–252

Objectives. Prosthetic joint infection (PJI) diagnosis is a major challenge in orthopaedics, and no reliable parameters have been established for accurate, preoperative predictions in the differential diagnosis of aseptic loosening or PJI. This study surveyed factors in synovial fluid (SF) for improving PJI diagnosis. Methods. We enrolled 48 patients (including 39 PJI and nine aseptic loosening cases) who required knee/hip revision surgery between January 2016 and December 2017. The PJI diagnosis was established according to the Musculoskeletal Infection Society (MSIS) criteria. SF was used to survey factors by protein array and enzyme-linked immunosorbent assay to compare protein expression patterns in SF among three groups (aseptic loosening and first- and second-stage surgery). We compared routine clinical test data, such as C-reactive protein level and leucocyte number, with potential biomarker data to assess the diagnostic ability for PJI within the same patient groups. Results. Cut-off values of 1473 pg/ml, 359 pg/ml, and 8.45 pg/ml were established for interleukin (IL)-16, IL-18, and cysteine-rich with EGF-like domains 2 (CRELD2), respectively. Receiver operating characteristic curve analysis showed that these factors exhibited an accuracy of 1 as predictors of PJI. These factors represent potential biomarkers for decisions associated with prosthesis reimplantation based on their ability to return to baseline values following the completion of debridement. Conclusion. IL-16, IL-18, and CRELD2 were found to be potential biomarkers for PJI diagnosis, with SF tests outperforming blood tests in accuracy. These factors could be useful for assessing successful debridement based on their ability to return to baseline values following the completion of debridement. Cite this article: M-F. Chen, C-H. Chang, L-Y. Yang, P-H. Hsieh, H-N. Shih, S. W. N. Ueng, Y. Chang. Synovial fluid interleukin-16, interleukin-18, and CRELD2 as novel biomarkers of prosthetic joint infections. Bone Joint Res 2019;8:179–188. DOI: 10.1302/2046-3758.84.BJR-2018-0291.R1

Aims. The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. Methods. In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots. Results. In all, 1,138 patients (242 males; 896 females; mean age 1.5 years (SD 1.79; 0 to 10) were included in this study. The area under the receiver operating characteristic curve, sensitivity, and specificity of the deep learning system for diagnosing hip dislocation were 0.975, 276/289 (95.5%), and 1,978/1,987 (99.5%), respectively. Compared with clinical diagnoses, the Bland-Altman 95% limits of agreement for acetabular index, as determined by the deep learning system from the radiographs of non-dislocated and dislocated hips, were -3.27° - 2.94° and -7.36° - 5.36°, respectively (p < 0.001). Conclusion. The deep learning system was highly consistent, more convenient, and more effective for diagnosing DDH compared with clinician-led diagnoses. Deep learning systems should be considered for analysis of anteroposterior pelvic radiographs when diagnosing DDH. The deep learning system will improve the current artificially complicated screening referral process. Cite this article: Bone Joint J 2020;102-B(11):1574–1581

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

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Excellent outcomes have been reported following CT-based robotic arm-assisted total hip arthroplasty (rTHA) compared with manual THA; however, its superiority over CT-based navigation THA (nTHA) remains unclear. This study aimed to determine whether a CT-based robotic arm-assisted system helps surgeons perform accurate cup placement, minimizes leg length, and offsets discrepancies more than a CT-based navigation system.

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

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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.

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

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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.

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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.