Aims. Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting
Telehealth has the potential to change the way we approach patient care. From virtual consenting to reducing carbon emissions, costs, and waiting times, it is a powerful tool in our clinical armamentarium. There is mounting evidence that remote diagnostic evaluation and decision-making have reached an acceptable level of accuracy and can safely be adopted in orthopaedic surgery. Furthermore, patients’ and surgeons’ satisfaction with virtual appointments are comparable to in-person consultations. Challenges to the widespread use of telehealth should, however, be acknowledged and include the cost of installation, training, maintenance, and accessibility. It is also vital that
Aims. To explore key stakeholder views around feasibility and acceptability of trials seeking to prevent post-traumatic osteoarthritis (PTOA) following knee injury, and provide guidance for next steps in PTOA trial design. Methods. Healthcare professionals,
Aims. The aims of this study were to develop an automatic system capable of calculating four radiological measurements used in the diagnosis and monitoring of cerebral palsy (CP)-related hip disease, and to demonstrate that these measurements are sufficiently accurate to be used in clinical practice. Methods. We developed a machine-learning system to automatically measure Reimer’s migration percentage (RMP), acetabular index (ACI), head shaft angle (HSA), and neck shaft angle (NSA). The system automatically locates points around the femoral head and acetabulum on pelvic radiographs, and uses these to calculate measurements. The system was evaluated on 1,650 pelvic radiographs of children with CP (682 females and 968 males, mean age 8.3 years (SD 4.5)). Each radiograph was manually measured by five clinical experts. Agreement between the manual clinical measurements and the automatic system was assessed by mean absolute deviation (MAD) from the mean manual measurement, type 1 and type 2 intraclass correlation coefficients (ICCs), and a linear mixed-effects model (LMM) for assessing bias. Results. The MAD scores were 5.7% (SD 8.5%) for RMP, 4.3° (SD 5.4°) for ACI, 5.0° (SD 5.2°) for NSA, and 5.7° (SD 6.1°) for HSA. Overall ICCs quantifying the agreement between the mean manual measurement and the automatic results were 0.91 for RMP, 0.66 for ACI, 0.85 for NSA, and 0.73 for HSA. The LMM showed no statistically significant bias. Conclusion. The results showed excellent agreement between the manual and automatic measurements for RMP, good agreement for NSA, and moderate agreement for HSA and ACI. The performance of the system is sufficient for application in clinical practice to support the assessment of hip migration based on RMP. The system has the potential to save
Aims. Periprosthetic hip-joint infection is a multifaceted and highly detrimental outcome for patients and
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,
Periprosthetic joint infection (PJI) represents a complex challenge in orthopaedic surgery associated with substantial morbidity and healthcare expenditures. The debridement, antibiotics, and implant retention (DAIR) protocol is a viable treatment, offering several advantages over exchange arthroplasty. With the evolution of treatment strategies, considerable efforts have been directed towards enhancing the efficacy of DAIR, including the development of a phased debridement protocol for acute PJI management. This article provides an in-depth analysis of DAIR, presenting the outcomes of single-stage, two-stage, and repeated DAIR procedures. It delves into the challenges faced, including patient heterogeneity, pathogen identification, variability in surgical techniques, and antibiotics selection. Moreover, critical factors that influence the decision-making process between single- and two-stage DAIR protocols are addressed, including team composition, timing of the intervention, antibiotic regimens, and both anatomical and implant-related considerations. By providing a comprehensive overview of DAIR protocols and their clinical implications, this annotation aims to elucidate the advancements, challenges, and potential future directions in the application of DAIR for PJI management. It is intended to equip
Aims. Patients with cauda equina syndrome (CES) require emergency imaging and surgical decompression. The severity and type of symptoms may influence the timing of imaging and surgery, and help predict the patient’s prognosis. Categories of CES attempt to group patients for management and prognostication purposes. We aimed in this study to assess the inter-rater reliability of dividing patients with CES into categories to assess whether they can be reliably applied in clinical practice and in research. Methods. A literature review was undertaken to identify published descriptions of categories of CES. A total of 100 real anonymized clinical vignettes of patients diagnosed with CES from the Understanding Cauda Equina Syndrome (UCES) study were reviewed by consultant spinal surgeons, neurosurgical registrars, and medical students. All were provided with published category definitions and asked to decide whether each patient had ‘suspected CES’; ‘early CES’; ‘incomplete CES’; or ‘CES with urinary retention’. Inter-rater agreement was assessed for all categories, for all raters, and for each group of raters using Fleiss’s kappa. Results. Each of the 100 participants were rated by four medical students, five neurosurgical registrars, and four consultant spinal surgeons. No groups achieved reasonable inter-rater agreement for any of the categories. CES with retention versus all other categories had the highest inter-rater agreement (kappa 0.34 (95% confidence interval 0.27 to 0.31); minimal agreement). There was no improvement in inter-rater agreement with clinical experience. Across all categories, registrars agreed with each other most often (kappa 0.41), followed by medical students (kappa 0.39). Consultant spinal surgeons had the lowest inter-rater agreement (kappa 0.17). Conclusion. Inter-rater agreement for categorizing CES is low among
Aims. Frailty greatly increases the risk of adverse outcome of trauma in older people. Frailty detection tools appear to be unsuitable for use in traumatically injured older patients. We therefore aimed to develop a method for detecting frailty in older people sustaining trauma using routinely collected clinical data. Methods. We analyzed prospectively collected registry data from 2,108 patients aged ≥ 65 years who were admitted to a single major trauma centre over five years (1 October 2015 to 31 July 2020). We divided the sample equally into two, creating derivation and validation samples. In the derivation sample, we performed univariate analyses followed by multivariate regression, starting with 27 clinical variables in the registry to predict Clinical Frailty Scale (CFS; range 1 to 9) scores. Bland-Altman analyses were performed in the validation cohort to evaluate any biases between the Nottingham Trauma Frailty Index (NTFI) and the CFS. Results. In the derivation cohort, five of the 27 variables were strongly predictive of the CFS (regression coefficient B = 6.383 (95% confidence interval 5.03 to 7.74), p < 0.001): age, Abbreviated Mental Test score, admission haemoglobin concentration (g/l), pre-admission mobility (needs assistance or not), and mechanism of injury (falls from standing height). In the validation cohort, there was strong agreement between the NTFI and the CFS (mean difference 0.02) with no apparent systematic bias. Conclusion. We have developed a clinically applicable tool using easily and routinely measured physiological and functional parameters, which
Aims. Occult (clinical) injuries represent 15% of all scaphoid fractures, posing significant challenges to the
Aims. The primary aim of this study was to assess the feasibility of recruiting and retaining patients to a patient-blinded randomized controlled trial comparing corticosteroid injection (CSI) to autologous protein solution (APS) injection for the treatment of subacromial shoulder pain in a community care setting. The study focused on recruitment rates and retention of participants throughout, and collected data on the interventions’ safety and efficacy. Methods. Participants were recruited from two community musculoskeletal treatment centres in the UK. Patients were eligible if aged 18 years or older, and had a clinical diagnosis of subacromial impingement syndrome which the treating
Aims. To assess the feasibility of a randomized controlled trial (RCT) that compares three treatments for acetabular fractures in older patients: surgical fixation, surgical fixation and hip arthroplasty (fix-and-replace), and non-surgical treatment. Methods. Patients were recruited from seven UK NHS centres and randomized to a three-arm pilot trial if aged older than 60 years and had a displaced acetabular fracture. Feasibility outcomes included patients’ willingness to participate,
Aims. The aim of this study was to develop and evaluate a deep learning-based model for classification of hip fractures to enhance diagnostic accuracy. Methods. A retrospective study used 5,168 hip anteroposterior radiographs, with 4,493 radiographs from two institutes (internal dataset) for training and 675 radiographs from another institute for validation. A convolutional neural network (CNN)-based classification model was trained on four types of hip fractures (Displaced, Valgus-impacted, Stable, and Unstable), using DAMO-YOLO for data processing and augmentation. The model’s accuracy, sensitivity, specificity, Intersection over Union (IoU), and Dice coefficient were evaluated. Orthopaedic surgeons’ diagnoses served as the reference standard, with comparisons made before and after artificial intelligence assistance. Results. The accuracy, sensitivity, specificity, IoU, and Dice coefficients of the model for the four fracture categories in the internal dataset were as follows: Displaced (1.0, 0.79, 1.0, 0.70, 0.82), Valgus-impacted (1.0, 0.80, 1.0, 0.70, 0.82), Stable (0.99, 0.95, 0.99, 0.83, 0.89), and Unstable (1.0, 0.98, 0.99, 0.86, 0.92), respectively. For the external validation dataset, the sensitivity and specificity were as follows: Displaced (0.83, 0.94), Valgus-impacted (0.89, 0.90), Stable (0.88, 0.95), and Unstable (0.85, 0.99), respectively. The overall means (Micro AVG and Macro AVG) for the external dataset were Micro AVG (0.83 (SD 0.05), 0.96 (SD 0.01)) and Macro AVG (0.69 (SD 0.02), 0.95 (SD 0.02)), respectively. Conclusion. Compared to human diagnosis alone, our study demonstrates that the developed model significantly improves the accuracy of detecting and classifying hip fractures. Our model has shown great potential in assisting
Aims. Hip disease is common in children with cerebral palsy (CP) and can decrease quality of life and function. Surveillance programmes exist to improve outcomes by treating hip disease at an early stage using radiological surveillance. However, studies and surveillance programmes report different radiological outcomes, making it difficult to compare. We aimed to identify the most important radiological measurements and develop a core measurement set (CMS) for clinical practice, research, and surveillance programmes. Methods. A systematic review identified a list of measurements previously used in studies reporting radiological hip outcomes in children with CP. These measurements informed a two-round Delphi study, conducted among orthopaedic surgeons and specialist physiotherapists. Participants rated each measurement on a nine-point Likert scale (‘not important’ to ‘critically important’). A consensus meeting was held to finalize the CMS. Results. Overall, 14 distinct measurements were identified in the systematic review, with Reimer’s migration percentage being the most frequently reported. These measurements were presented over the two rounds of the Delphi process, along with two additional measurements that were suggested by participants. Ultimately, two measurements, Reimer’s migration percentage and femoral head-shaft angle, were included in the CMS. Conclusion. This use of a minimum standardized set of measurements has the potential to encourage uniformity across hip surveillance programmes, and may streamline the development of tools, such as artificial intelligence systems to automate the analysis in surveillance programmes. This core set should be the minimum requirement in clinical studies, allowing
Aims. The aim of this study was to gain a consensus for best practice of the assessment and management of children with idiopathic toe walking (ITW) in order to provide a benchmark for practitioners and guide the best consistent care. Methods. An established Delphi approach with predetermined steps and degree of agreement based on a standardized protocol was used to determine consensus. The steering group members and Delphi survey participants included members from the British Society of Children’s Orthopaedic Surgery (BSCOS) and the Association of Paediatric Chartered Physiotherapists (APCP). The statements included definition, assessment, treatment indications, nonoperative and operative interventions, and outcomes. Descriptive statistics were used for analysis of the Delphi survey results. The AGREE checklist was followed for reporting the results. Results. A total of 227 participants (54% APCP and 46% BSCOS members) completed the first round, and 222 participants (98%) completed the second round. Out of 54 proposed statements included in the first round Delphi, 17 reached ‘consensus in’, no statements reached ‘consensus out’, and 37 reached ‘no consensus’. These 37 statements were then discussed, reworded, amalgamated, or deleted before the second round Delphi of 29 statements. A total of 12 statements reached ‘consensus in’, four ‘consensus out’, and 13 ‘no consensus’. In the final consensus meeting, 13 statements were voted upon. Five were accepted, resulting in a total of 31 approved statements. Conclusion. In the aspects of practice where sufficient evidence is not available, a consensus statement can provide a strong body of opinion that acts as a benchmark for excellence in clinical care. This statement can assist
Aims. This study evaluated the effect of treating
Aims. A national screening programme has existed in the UK for the diagnosis of developmental dysplasia of the hip (DDH) since 1969. However, every aspect of screening and treatment remains controversial. Screening programmes throughout the world vary enormously, and in the UK there is significant variation in screening practice and treatment pathways. We report the results of an attempt by the British Society for Children’s Orthopaedic Surgery (BSCOS) to identify a nationwide consensus for the management of DDH in order to unify treatment and suggest an approach for screening. Methods. A Delphi consensus study was performed among the membership of BSCOS. Statements were generated by a steering group regarding aspects of the management of DDH in children aged under three months, namely screening and surveillance (15 questions), the technique of ultrasound scanning (eight questions), the initiation of treatment (19 questions), care during treatment with a splint (ten questions), and on quality, governance, and research (eight questions). A two-round Delphi process was used and a consensus document was produced at the final meeting of the steering group. Results. A total of 60 statements were graded by 128
Aims. The Birmingham Orthopaedic Oncology Meeting (BOOM), held in January 2024, convened 309 delegates from 53 countries to discuss and refine 21 consensus statements on the optimal management of chondrosarcoma. Methods. With representation from Europe (43%; n = 133), North America (17%; n = 53), South America (16%; n = 49), Asia (13%; n = 40), Australasia (5%; n = 16), the Middle East (4%; n = 12), and Africa (2%; n = 6), the combined experience of treating bone sarcomas among attendees totalled approximately 30,000 cases annually, equivalent to 66 years of experience in the UK alone. The meeting’s process began with the formation of a local organizing committee, regional leads, and a scientific committee comprising representatives from 150 specialist units across 47 countries. Supported by major orthopaedic oncology organizations, the meeting used a modified Delphi process to develop consensus statements through online questionnaires, thematic groupings, narrative reviews, and anonymous pre-meeting polling. Results. Strong (> 80%) consensus was achieved on 19 out of 21 statements, reflecting agreement among delegates. Key areas of consensus included the role of radiology in diagnosis and surveillance, the management of locally recurrent disease, and the treatment of dedifferentiated chondrosarcoma. Notably, there was agreement that routine chemotherapy has no role in chondrosarcoma treatment, and radiological surveillance is safe for intraosseous chondrosarcomas. Despite the overall consensus, areas of controversy remain, particularly regarding the treatment of atypical cartilage tumours and surgical margins. These unresolved issues underscore the need for further research and collaboration within the orthopaedic oncology community. Conclusion. BOOM represents the largest global consensus meeting in orthopaedic oncology, providing valuable guidance for
Aims. The optimal management of posterior malleolar ankle fractures, a prevalent type of ankle trauma, is essential for improved prognosis. However, there remains a debate over the most effective surgical approach, particularly between screw and plate fixation methods. This study aims to investigate the differences in outcomes associated with these fixation techniques. Methods. We conducted a comprehensive review of clinical trials comparing anteroposterior (A-P) screws, posteroanterior (P-A) screws, and plate fixation. Two investigators validated the data sourced from multiple databases (MEDLINE, EMBASE, and Web of Science). Following PRISMA guidelines, we carried out a network meta-analysis (NMA) using visual analogue scale and American Orthopaedic Foot and Ankle Score (AOFAS) as primary outcomes. Secondary outcomes included range of motion limitations, radiological outcomes, and complication rates. Results. The NMA encompassed 13 studies, consisting of four randomized trials and eight retrospective ones. According to the surface under the cumulative ranking curve-based ranking, the A-P screw was ranked highest for improvements in AOFAS and exhibited lowest in infection and peroneal nerve injury incidence. The P-A screws, on the other hand, excelled in terms of VAS score improvements. Conversely, posterior buttress plate fixation showed the least incidence of osteoarthritis grade progression, postoperative articular step-off ≥ 2 mm, nonunions, and loss of ankle dorsiflexion ≥ 5°, though it underperformed in most other clinical outcomes. Conclusion. The NMA suggests that open plating is more likely to provide better radiological outcomes, while screw fixation may have a greater potential for superior functional and pain results. Nevertheless,
Aims. This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images. Methods. The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm. 3. ). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis. Results. CT-aBMD was successfully measured in 976/978 hips (99.8%). A significant correlation was found between CT-aBMD and DXA-BMD (r = 0.941; p < 0.001). In the ROC analysis, the area under the curve to diagnose osteoporosis was 0.976. The diagnostic sensitivity and specificity were 88.9% and 96%, respectively, with the cutoff set at 0.625 g/cm. 2. . Conclusion. Accurate DXA-BMD measurements and diagnosis of osteoporosis were performed from CT images using the system developed herein. As the models are open-source,