Aims. The management of fractures of the medial epicondyle is one of the greatest controversies in paediatric fracture care, with uncertainty concerning the need for surgery. The British Society of Children’s Orthopaedic Surgery prioritized this as their most important research question in paediatric trauma. This is the protocol for a randomized controlled, multicentre, prospective superiority trial of operative fixation versus nonoperative treatment for displaced medial epicondyle fractures: the Surgery or Cast of the EpicoNdyle in Children’s Elbows (SCIENCE) trial. Methods. Children aged seven to 15 years old inclusive, who have sustained a displaced fracture of the medial epicondyle, are eligible to take part. Baseline function using the Patient-Reported Outcomes Measurement Information System (PROMIS) upper limb score, pain measured using the Wong Baker FACES pain scale, and quality of life (QoL) assessed with the EuroQol five-dimension questionnaire for younger patients (EQ-5D-Y) will be collected. Each patient will be randomly allocated (1:1, stratified using a minimization algorithm by centre and initial elbow dislocation status (i.e. dislocated or not-dislocated at presentation to the emergency department)) to either a regimen of the operative fixation or non-surgical treatment. Outcomes. At six weeks, and three, six, and 12 months, data on function, pain, sports/music participation, QoL, immobilization, and analgesia will be collected. These will also be repeated annually until the child reaches the age of 16 years. Four weeks after injury, the main outcomes plus data on complications, resource use, and school absence will be collected. The primary outcome is the PROMIS upper limb score at 12 months post-randomization. All data will be obtained through electronic questionnaires completed by the participants and/or parents/guardians. The NHS number of participants will be stored to enable future data linkage to sources of routinely collected data (i.e. Hospital Episode Statistics). Cite this article: Bone Jt Open 2024;5(1):69–77

Aims. The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients. Methods. Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset. Results. A total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752. Conclusion. This study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment. Cite this article: Bone Joint J 2023;105-B(6):702–710

Aims. To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA. Methods. Data were obtained from the American College of Surgeons National Quality Improvement Programme (ACS-NSQIP) database from the years 2018 to 2020. Patients with elective, unilateral rTKA procedures and a total hospital length of stay between zero and four days were included. Demographic, preoperative, and intraoperative variables were analyzed. A multivariable logistic regression (MLR) model and various machine learning techniques were compared using area under the curve (AUC), calibration, and decision curve analysis. Important and significant variables were identified from the models. Results. Of the 5,600 patients included in this study, 342 (6.1%) underwent SDD. The random forest (RF) model performed the best overall, with an internally validated AUC of 0.810. The ten crucial factors favoring SDD in the RF model include operating time, anaesthesia type, age, BMI, American Society of Anesthesiologists grade, race, history of diabetes, rTKA type, sex, and smoking status. Eight of these variables were also found to be significant in the MLR model. Conclusion. The RF model displayed excellent accuracy and identified clinically important variables for determining candidates for SDD following rTKA. Machine learning techniques such as RF will allow clinicians to accurately risk-stratify their patients preoperatively, in order to optimize resources and improve patient outcomes. Cite this article: Bone Jt Open 2023;4(6):399–407

Aims. To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Methods. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation. Results. For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts. Conclusion. The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts. Cite this article: Bone Joint J 2024;106-B(7):688–695

Aims. Ganz’s studies made it possible to address joint deformities on both the femoral and acetabular side brought about by Perthes’ disease. Femoral head reduction osteotomy (FHRO) was developed to improve joint congruency, along with periacetabular osteotomy (PAO), which may enhance coverage and containment. The purpose of this study is to show the clinical and morphological outcomes of the technique and the use of an implemented planning approach. Methods. From September 2015 to December 2021, 13 FHROs were performed on 11 patients for Perthes’ disease in two centres. Of these, 11 hips had an associated PAO. A specific CT- and MRI-based protocol for virtual simulation of the corrections was developed. Outcomes were assessed with radiological parameters (sphericity index, extrusion index, integrity of the Shenton’s line, lateral centre-edge angle (LCEA), Tönnis angle), and clinical parameters (range of motion, visual analogue scale (VAS) for pain, Merle d'Aubigné-Postel score, modified Harris Hip Score (mHHS), and EuroQol five-dimension five-level health questionnaire (EQ-5D-5L)). Early and late complications were reported. Results. The mean follow-up was 39.7 months (standard deviation (SD) 26.4). The mean age at surgery was 11.4 years (SD 1.6). No major complications were recorded. One patient required a total hip arthroplasty. Mean femoral head sphericity increased from 46.8% (SD 9.34%) to 70.2% (SD 15.44; p < 0.001); mean LCEA from 19.2° (SD 9.03°) to 44° (SD 10.27°; p < 0.001); mean extrusion index from 37.8 (SD 8.70) to 7.5 (SD 9.28; p < 0.001); and mean Tönnis angle from 16.5° (SD 12.35°) to 4.8° (SD 4.05°; p = 0.100). The mean VAS improved from 3.55 (SD 3.05) to 1.22 (1.72; p = 0.06); mean Merle d’Aubigné-Postel score from 14.55 (SD 1.74) to 16 (SD 1.6; p = 0.01); and mean mHHS from 60.6 (SD 18.06) to 81 (SD 6.63; p = 0.021). The EQ-5D-5L also showed significant improvements. Conclusion. FHRO associated with periacetabular procedures is a safe technique that showed improved functional, clinical, and morphological outcomes in Perthes’ disease. The newly introduced simulation and planning algorithm may help to further refine the technique. Cite this article: Bone Joint J 2024;106-B(5 Supple B):40–46

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

Aims. Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. Methods. The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy. Results. Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. Conclusion. These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929–937

Aims. This study aimed, through bioinformatics analysis, to identify the potential diagnostic markers of osteoarthritis, and analyze the role of immune infiltration in synovial tissue. Methods. The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by R software. Functional enrichment analyses were performed and protein-protein interaction networks (PPI) were constructed. Then the hub genes were screened. Biomarkers with high value for the diagnosis of early osteoarthritis (OA) were validated by GEO datasets. Finally, the CIBERSORT algorithm was used to evaluate the immune infiltration between early-stage OA and end-stage OA, and the correlation between the diagnostic marker and infiltrating immune cells was analyzed. Results. A total of 88 DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that DEGs were significantly enriched in leucocyte migration and interleukin (IL)-17 signalling pathways. Disease ontology (DO) indicated that DEGs were mostly enriched in rheumatoid arthritis. Six hub genes including FosB proto-oncogene, AP-1 transcription factor subunit (FOSB); C-X-C motif chemokine ligand 2 (CXCL2); CXCL8; IL-6; Jun proto-oncogene, AP-1 transcription factor subunit (JUN); and Activating transcription factor 3 (ATF3) were identified and verified by GEO datasets. ATF3 (area under the curve = 0.975) turned out to be a potential biomarker for the diagnosis of early OA. Several infiltrating immune cells varied significantly between early-stage OA and end-stage OA, such as resting NK cells (p = 0.016), resting dendritic cells (p = 0.043), and plasma cells (p = 0.043). Additionally, ATF3 was significantly correlated with resting NK cells (p = 0.034), resting dendritic cells (p = 0.026), and regulatory T cells (Tregs, p = 0.018). Conclusion. ATF3 may be a potential diagnostic marker for early diagnosis and treatment of OA, and immune cell infiltration provides new perspectives for understanding the mechanism during OA progression. Cite this article: Bone Joint Res 2022;11(9):679–689

In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article: Bone Joint J 2021;103-B(9):1442–1448

Aims. This study aimed to develop a virtual clinic for the purpose of reducing face-to-face orthopaedic consultations. Patients and Methods. Anonymized experts (hip and knee arthroplasty patients, surgeons, physiotherapists, radiologists, and arthroplasty practitioners) gave feedback via a Delphi Consensus Technique. This consisted of an iterative sequence of online surveys, during which virtual documents, made up of a patient-reported questionnaire, standardized radiology report, and decision-guiding algorithm, were modified until consensus was achieved. We tested the patient-reported questionnaire on seven patients in orthopaedic clinics using a ‘think-aloud’ process to capture difficulties with its completion. Results. A patient-reported 13-item questionnaire was developed covering pain, mobility, and activity. The radiology report included up to ten items (e.g. progressive periprosthetic bone loss) depending on the type of arthroplasty. The algorithm concludes in one of three outcomes: review at surgeon’s discretion (three to 12 months); see at next available clinic; or long-term follow-up/discharge. Conclusion. The virtual clinic approach with attendant documents achieved consensus by orthopaedic experts, radiologists, and patients. The robust development and testing of this standardized virtual clinic provided a sound platform for organizations in the United Kingdom to adopt a virtual clinic approach for follow-up of hip and knee arthroplasty patients. Cite this article: Bone Joint J 2019;101-B:951–959

Aims. This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Methods. Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay. Results. The predictability of the ANN model, area under the curve (AUC) = 0.801, was similar to the logistic regression model (AUC = 0.796) and identified certain variables as important factors to predict same-day discharge. The ten most important factors favouring same-day discharge in the ANN model include preoperative sodium, preoperative international normalized ratio, BMI, age, anaesthesia type, operating time, dyspnoea status, functional status, race, anaemia status, and chronic obstructive pulmonary disease (COPD). Six of these variables were also found to be significant on logistic regression analysis. Conclusion. Both ANN modelling and logistic regression analysis revealed clinically important factors in predicting patients who can undergo safely undergo same-day discharge from an outpatient TKA. The ANN model provides a beneficial approach to help determine which perioperative factors can predict same-day discharge as of 2018 perioperative recovery protocols. Cite this article: Bone Joint J 2021;103-B(8):1358–1366

Aims. The aim of this study was to determine the impact of hospital-level service characteristics on hip fracture outcomes and quality of care processes measures. Methods. This was a retrospective analysis of publicly available audit data obtained from the National Hip Fracture Database (NHFD) 2018 benchmark summary and Facilities Survey. Data extraction was performed using a dedicated proforma to identify relevant hospital-level care process and outcome variables for inclusion. The primary outcome measure was adjusted 30-day mortality rate. A random forest-based multivariate imputation by chained equation (MICE) algorithm was used for missing value imputation. Univariable analysis for each hospital level factor was performed using a combination of Tobit regression, Siegal non-parametric linear regression, and Mann-Whitney U test analyses, dependent on the data type. In all analyses, a p-value < 0.05 denoted statistical significance. Results. Analyses included 176 hospitals, with a median of 366 hip fracture cases per year (interquartile range (IQR) 280 to 457). Aggregated data from 66,578 patients were included. The only identified hospital-level variable associated with the primary outcome of 30-day mortality was hip fracture trial involvement (no trial involvement: median 6.3%; trial involvement: median 5.7%; p = 0.039). Significant key associations were also identified between prompt surgery and presence of dedicated hip fracture sessions; reduced acute length of stay and both a higher number of hip fracture cases per year and more dedicated hip fracture operating lists; Best Practice Tariff attainment and greater number of hip fracture cases per year, more dedicated hip fracture operating lists, presence of a dedicated hip fracture ward, and hip fracture trial involvement. Conclusion. Exploratory analyses have identified that improved outcomes in hip fracture may be associated with hospital-level service characteristics, such as hip fracture research trial involvement, larger hip fracture volumes, and the use of theatre lists dedicated to hip fracture surgery. Further research using patient level data is warranted to corroborate these findings. Cite this article: Bone Joint J 2021;103-B(10):1627–1632

Aims. Current guidelines consider analyses of joint aspirates, including leucocyte cell count (LC) and polymorphonuclear percentage (PMN%) as a diagnostic mainstay of periprosthetic joint infection (PJI). It is unclear if these parameters are subject to a certain degree of variability over time. Therefore, the aim of this study was to evaluate the variation of LC and PMN% in patients with aseptic revision total knee arthroplasty (TKA). Methods. We conducted a prospective, double-centre study of 40 patients with 40 knee joints. Patients underwent joint aspiration at two different time points with a maximum period of 120 days in between these interventions and without any events such as other joint aspirations or surgeries. The main indications for TKA revision surgery were aseptic implant loosening (n = 24) and joint instability (n = 11). Results. Overall, 80 synovial fluid samples of 40 patients were analyzed. The average time period between the joint aspirations was 50 days (SD 32). There was a significantly higher percentage change in LC when compared to PMN% (44.1% (SD 28.6%) vs 27.3% (SD 23.7%); p = 0.003). When applying standard definition criteria, LC counts were found to skip back and forth between the two time points with exceeding the thresholds in up to 20% of cases, which was significantly more compared to PMN% for the European Bone and Joint Infection Society (EBJIS) criteria (p = 0.001), as well as for Musculoskeletal Infection Society (MSIS) (p = 0.029). Conclusion. LC and PMN% are subject to considerable variation. According to its higher interindividual variance, LC evaluation might contribute to false-positive or false-negative results in PJI assessment. Single LC testing prior to TKA revision surgery seems to be insufficient to exclude PJI. On the basis of the obtained results, PMN% analyses overrule LC measurements with regard to a conclusive diagnostic algorithm. Cite this article: Bone Jt Open 2021;2(8):566–572

The treatment of peri-prosthetic joint infection (PJI) of the ankle is not standardised. It is not clear whether an algorithm developed for hip and knee PJI can be used in the management of PJI of the ankle. We evaluated the outcome, at two or more years post-operatively, in 34 patients with PJI of the ankle, identified from a cohort of 511 patients who had undergone total ankle replacement. Their median age was 62.1 years (53.3 to 68.2), and 20 patients were women. Infection was exogenous in 28 (82.4%) and haematogenous in six (17.6%); 19 (55.9%) were acute infections and 15 (44.1%) chronic. Staphylococci were the cause of 24 infections (70.6%). Surgery with retention of one or both components was undertaken in 21 patients (61.8%), both components were replaced in ten (29.4%), and arthrodesis was undertaken in three (8.8%). An infection-free outcome with satisfactory function of the ankle was obtained in 23 patients (67.6%). The best rate of cure followed the exchange of both components (9/10, 90%). In the 21 patients in whom one or both components were retained, four had a relapse of the same infecting organism and three had an infection with another organism. Hence the rate of cure was 66.7% (14 of 21). In these 21 patients, we compared the treatment given to an algorithm developed for the treatment of PJI of the knee and hip. In 17 (80.9%) patients, treatment was not according to the algorithm. Most (11 of 17) had only one criterion against retention of one or both components. In all, ten of 11 patients with severe soft-tissue compromise as a single criterion had a relapse-free survival. We propose that the treatment concept for PJI of the ankle requires adaptation of the grading of quality of the soft tissues. Cite this article: Bone Joint J 2014;96-B:772–7

Aims. To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis. Methods. A total of 763,213 hip and knee joint arthroplasty episodes recorded in the National Joint Registry for England and Wales (NJR) and 105,407 episodes from the Norwegian Arthroplasty Register were used to model individual mortality risk over the first year after surgery using flexible parametric survival regression. Results. The one-year mortality rates in the NJR were 10.8 and 8.9 per 1,000 patient-years after hip and knee arthroplasty, respectively. The Norwegian mortality rates were 9.1 and 6.0 per 1,000 patient-years, respectively. The strongest predictors of death in the final models were age, sex, body mass index, and American Society of Anesthesiologists grade. Exposure variables related to the intervention, with the exception of knee arthroplasty type, did not add discrimination over patient factors alone. Discrimination was good in both cohorts, with c-indices above 0.76 for the hip and above 0.70 for the knee. Time-dependent Brier scores indicated appropriate estimation of the mortality rate (≤ 0.01, all models). Conclusion. Simple demographic and clinical information may be used to calculate an individualized estimation for one-year mortality risk after hip or knee arthroplasty (. https://jointcalc.shef.ac.uk. ). These models may be used to provide patients with an estimate of the risk of mortality after joint arthroplasty. Cite this article: Bone Joint Res 2020;9(11):808–820

Clinical prediction algorithms are used to differentiate transient synovitis from septic arthritis. These algorithms typically include the erythrocyte sedimentation rate (ESR), although in clinical practice measurement of the C-reactive protein (CRP) has largely replaced the ESR. We evaluated the use of CRP in a predictive algorithm. The records of 311 children with an effusion of the hip, which was confirmed on ultrasound, were reviewed (mean age 5.3 years (0.2 to 15.1)). Of these, 269 resolved without intervention and without long-term sequelae and were considered to have had transient synovitis. The remaining 42 underwent arthrotomy because of suspicion of septic arthritis. Infection was confirmed in 29 (18 had micro-organisms isolated and 11 had a high synovial fluid white cell count). In the remaining 13 no evidence of infection was found and they were also considered to have had transient synovitis. In total 29 hips were categorised as septic arthritis and 282 as transient synovitis. The temperature, weight-bearing status, peripheral white blood cell count and CRP was reviewed in each patient. A CRP > 20 mg/l was the strongest independent risk factor for septic arthritis (odds ratio 81.9, p < 0.001). A multivariable prediction model revealed that only two determinants (weight-bearing status and CRP > 20 mg/l) were independent in differentiating septic arthritis from transient synovitis. Individuals with neither predictor had a < 1% probability of septic arthritis, but those with both had a 74% probability of septic arthritis. A two-variable algorithm can therefore quantify the risk of septic arthritis, and is an excellent negative predictor.

The early failure and revision of bimodular primary total hip arthroplasty prostheses requires the identification of the risk factors for material loss and wear at the taper junctions through taper wear analysis. Deviations in taper geometries between revised and pristine modular neck tapers were determined using high resolution tactile measurements. A new algorithm was developed and validated to allow the quantitative analysis of material loss, complementing the standard visual inspection currently used. The algorithm was applied to a sample of 27 retrievals (in situ from 2.9 to 38.1 months) of the withdrawn Rejuvenate modular prosthesis. The mean wear volumes on the flat distal neck piece taper was 3.35 mm. 3. (0.55 to 7.57), mainly occurring in a characteristic pattern in areas with high mechanical loading. Wear volume tended to increase with time to revision (r² = 0.423, p = 0.001). Implant and patient specific data (offset, stem size, patient’s mass, age and body mass index) did not correlate with the amount of material loss observed (p >  0.078). Bilaterally revised implants showed higher amounts of combined total material loss and similar wear patterns on both sides. The consistent wear pattern found in this study has not been reported previously, suggesting that the device design and materials are associated with the failure of this prosthesis. Cite this article: Bone Joint J 2015;97-B:1350–7

Aims. While previously underappreciated, factors related to the spine contribute substantially to the risk of dislocation following total hip arthroplasty (THA). These factors must be taken into consideration during preoperative planning for revision THA due to recurrent instability. We developed a protocol to assess the functional position of the spine, the significance of these findings, and how to address different pathologies at the time of revision THA. Patients and Methods. Prospectively collected data on 111 patients undergoing revision THA for recurrent instability from January 2014 to January 2017 at two institutions were included (protocol group) and matched 1:1 to 111 revisions specifically performed for instability not using this protocol (control group). Mean follow-up was 2.8 years. Protocol patients underwent standardized preoperative imaging including supine and standing anteroposterior (AP) pelvis and lateral radiographs. Each case was scored according to the Hip-Spine Classification in Revision THA. Results. Survival free of dislocation at two years was 97% in the protocol group (three dislocations, all within three months of surgery) versus 84% in the control group (18 patients). Furthermore, 77% of the inappropriately positioned acetabular components would have been unrecognized by supine AP pelvis imaging alone. Conclusion. Using the Hip-Spine Classification System in revision THA, we demonstrated a significant decrease in the risk of recurrent instability compared with a control group. Without the use of this algorithm, 77% of inappropriately positioned acetabular components would have been unrecognized and incorrect treatment may have been instituted. Cite this article: Bone Joint J 2019;101-B:817–823

Aims. We aimed to describe the epidemiological, biological, and bacteriological characteristics of osteoarticular infections (OAIs) caused by Kingella kingae. Methods. The medical charts of all children presenting with OAIs to our institution over a 13-year period (January 2007 to December 2019) were reviewed. Among these patients, we extracted those which presented an OAI caused by K. kingae and their epidemiological data, biological results, and bacteriological aetiologies were assessed. Results. K. kingae was the main reported microorganism in our paediatric population, being responsible for 48.7% of OAIs confirmed bacteriologically. K. kingae affects primarily children aged between six months and 48 months. The highest prevalence of OAI caused by K. kingae was between seven months and 24 months old. After the patients were 27 months old, its incidence decreased significantly. The incidence though of infection throughout the year showed no significant differences. Three-quarters of patients with an OAI caused by K. kingae were afebrile at hospital admission, 11% had elevated WBCs, and 61.2% had abnormal CRPs, whereas the ESR was increased in 75%, constituting the most significant predictor of an OAI. On MRI, we noted 53% of arthritis affecting mostly the knee and 31% of osteomyelitis located primarily in the foot. Conclusion. K. kingae should be recognized currently as the primary pathogen causing OAI in children younger than 48 months old. Diagnosis of an OAI caused by K. kingae is not always obvious, since this infection may occur with a mild-to-moderate clinical and biological inflammatory response. Extensive use of nucleic acid amplification assays improved the detection of fastidious pathogens and has increased the observed incidence of OAI, especially in children aged between six months and 48 months. We propose the incorporation of polymerase chain reaction assays into modern diagnostic algorithms for OAIs to better identify the bacteriological aetiology of OAIs. Cite this article: Bone Joint J 2021;103-B(3):578–583

Cite this article: Bone Joint Res 2023;12(8):494–496.