Aims

Approximately 10% to 20% of knee arthroplasty patients are not satisfied with the result, while a clear indication for revision surgery might not be present. Therapeutic options for these patients, who often lack adequate quadriceps strength, are limited. Therefore, the primary aim of this study was to evaluate the clinical effect of a novel rehabilitation protocol that combines low-load resistance training (LL-RT) with blood flow restriction (BFR).

Aims. The aim of this audit was to assess and improve the completeness and accuracy of the National Joint Registry (NJR) dataset for arthroplasty of the elbow. Methods. It was performed in two phases. In Phase 1, the completeness was assessed by comparing the NJR elbow dataset with the NHS England Hospital Episode Statistics (HES) data between April 2012 and April 2020. In order to assess the accuracy of the data, the components of each arthroplasty recorded in the NJR were compared to the type of arthroplasty which was recorded. In Phase 2, a national collaborative audit was undertaken to evaluate the reasons for unmatched data, add missing arthroplasties, and evaluate the reasons for the recording of inaccurate arthroplasties and correct them. Results. Phase 1 identified 5,539 arthroplasties in HES which did not match an arthroplasty on the NJR, and 448 inaccurate arthroplasties from 254 hospitals. Most mismatched procedures (3,960 procedures; 71%) were radial head arthroplasties (RHAs). In Phase 2, 142 NHS hospitals with 3,640 (66%) mismatched and 314 (69%) inaccurate arthroplasties volunteered to assess their records. A large proportion of the unmatched data (3,000 arthroplasties; 82%) were confirmed as being missing from the NJR. The overall rate of completeness of the NJR elbow dataset improved from 63% to 83% following phase 2, and the completeness of total elbow arthroplasty data improved to 93%. Missing RHAs had the biggest impact on the overall completeness, but through the audit the number of RHAs in the NJR nearly doubled and completeness increased from 35% to 70%. The accuracy of data was 94% and improved to 98% after correcting 212 of the 448 inaccurately recorded arthroplasties. Conclusion. The rate of completeness of the NJR total elbow arthroplasty dataset is currently 93% and the accuracy is 98%. This audit identified challenges of data capture with regard to RHAs. Collaboration with a trauma and orthopaedic trainees through the British Orthopaedic Trainee Association improved the completeness and accuracy of the NJR elbow dataset, which will improve the validity of the reports and of the associated research. Cite this article: Bone Joint J 2024;106-B(12):1461–1468

As advancements in total knee arthroplasty progress at an exciting pace, two areas are of special interest, as they directly impact implant design and surgical decision making. Knee morphometry considers the three-dimensional shape of the articulating surfaces within the knee joint, and knee phenotyping provides the ability to categorize alignment into practical groupings that can be used in both clinical and research settings. This annotation discusses the details of these concepts, and the ways in which they are helping us better understand the individual subtleties of each patient’s knee.

Cite this article: Bone Joint J 2024;106-B(12):1363–1368.

Aims

For displaced femoral neck fractures (FNFs) in geriatric patients, there remains uncertainty regarding the effect of total hip arthroplasty (THA) compared with hemiarthroplasty (HA) in the guidelines. We aimed to compare 90-day surgical readmission, in-hospital complications, and charges between THA and HA in these patients.

Aims

The objective of this study was to compare simulated range of motion (ROM) for reverse total shoulder arthroplasty (rTSA) with and without adjustment for scapulothoracic orientation in a global reference system. We hypothesized that values for simulated ROM in preoperative planning software with and without adjustment for scapulothoracic orientation would be significantly different.

Aims

We aimed to compare reoperations following distal radial fractures (DRFs) managed with early fixation versus delayed fixation following initial closed reduction (CR).

Aims. The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs. Methods. The CNN was trained and tested on radiographs sourced from 11 hospitals in Australia and externally validated on radiographs from the Netherlands. Each radiograph was paired with corresponding CT scans to serve as the reference standard based on dual independent evaluation by trained researchers and attending orthopaedic surgeons. Presence of a fracture, classification (non- to minimally displaced; two-part, multipart, and glenohumeral dislocation), and four characteristics were determined on 2D and 3D CT scans and subsequently allocated to each series of radiographs. Fracture characteristics included greater tuberosity displacement ≥ 1 cm, NSA ≤ 100°, shaft translation (0% to < 75%, 75% to 95%, > 95%), and the extent of articular involvement (0% to < 15%, 15% to 35%, or > 35%). Results. For detection and classification, the algorithm was trained on 1,709 radiographs (n = 803), tested on 567 radiographs (n = 244), and subsequently externally validated on 535 radiographs (n = 227). For characterization, healthy shoulders and glenohumeral dislocation were excluded. The overall accuracy for fracture detection was 94% (area under the receiver operating characteristic curve (AUC) = 0.98) and for classification 78% (AUC 0.68 to 0.93). Accuracy to detect greater tuberosity fracture displacement ≥ 1 cm was 35.0% (AUC 0.57). The CNN did not recognize NSAs ≤ 100° (AUC 0.42), nor fractures with ≥ 75% shaft translation (AUC 0.51 to 0.53), or with ≥ 15% articular involvement (AUC 0.48 to 0.49). For all objectives, the model’s performance on the external dataset showed similar accuracy levels. Conclusion. CNNs proficiently rule out proximal humerus fractures on plain radiographs. Despite rigorous training methodology based on CT imaging with multi-rater consensus to serve as the reference standard, artificial intelligence-driven classification is insufficient for clinical implementation. The CNN exhibited poor diagnostic ability to detect greater tuberosity displacement ≥ 1 cm and failed to identify NSAs ≤ 100°, shaft translations, or articular fractures. Cite this article: Bone Joint J 2024;106-B(11):1348–1360

Aims

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials.

Aims

The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population.

Aims

The risk factors for recurrent instability (RI) following a primary traumatic anterior shoulder dislocation (PTASD) remain unclear. In this study, we aimed to determine the rate of RI in a large cohort of patients managed nonoperatively after PTASD and to develop a clinical prediction model.

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To determine if patient ethnicity among patients with a hip fracture influences the type of fracture, surgical care, and outcome.

Aims

This study aimed to gather insights from elbow experts using the Delphi method to evaluate the influence of patient characteristics and fracture morphology on the choice between operative and nonoperative treatment for coronoid fractures.

Aims. To propose a new method for evaluating paediatric radial neck fractures and improve the accuracy of fracture angulation measurement, particularly in younger children, and thereby facilitate planning treatment in this population. Methods. Clinical data of 117 children with radial neck fractures in our hospital from August 2014 to March 2023 were collected. A total of 50 children (26 males, 24 females, mean age 7.6 years (2 to 13)) met the inclusion criteria and were analyzed. Cases were excluded for the following reasons: Judet grade I and Judet grade IVb (> 85° angulation) classification; poor radiograph image quality; incomplete clinical information; sagittal plane angulation; severe displacement of the ulna fracture; and Monteggia fractures. For each patient, standard elbow anteroposterior (AP) view radiographs and corresponding CT images were acquired. On radiographs, Angle P (complementary to the angle between the long axis of the radial head and the line perpendicular to the physis), Angle S (complementary to the angle between the long axis of the radial head and the midline through the proximal radial shaft), and Angle U (between the long axis of the radial head and the straight line from the distal tip of the capitellum to the coronoid process) were identified as candidates approximating the true coronal plane angulation of radial neck fractures. On the coronal plane of the CT scan, the angulation of radial neck fractures (CTa) was measured and served as the reference standard for measurement. Inter- and intraobserver reliabilities were assessed by Kappa statistics and intraclass correlation coefficient (ICC). Results. Angle U showed the strongest correlation with CTa (p < 0.001). In the analysis of inter- and intraobserver reliability, Kappa values were significantly higher for Angles S and U compared with Angle P. ICC values were excellent among the three groups. Conclusion. Angle U on AP view was the best substitute for CTa when evaluating radial neck fractures in children. Further studies are required to validate this method. Cite this article: Bone Joint J 2024;106-B(9):964–969

Aims

The primary objective of this study was to develop a validated classification system for assessing iatrogenic bone trauma and soft-tissue injury during total hip arthroplasty (THA). The secondary objective was to compare macroscopic bone trauma and soft-tissues injury in conventional THA (CO THA) versus robotic arm-assisted THA (RO THA) using this classification system.

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.

Aims. Conventional patient-reported surveys, used for patients undergoing total hip arthroplasty (THA), are limited by subjectivity and recall bias. Objective functional evaluation, such as gait analysis, to delineate a patient’s functional capacity and customize surgical interventions, may address these shortcomings. This systematic review endeavours to investigate the application of objective functional assessments in appraising individuals undergoing THA. Methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied. Eligible studies of THA patients that conducted at least one type of objective functional assessment both pre- and postoperatively were identified through Embase, Medline/PubMed, and Cochrane Central database-searching from inception to 15 September 2023. The assessments included were subgrouped for analysis: gait analysis, motion analysis, wearables, and strength tests. Results. A total of 130 studies using 15 distinct objective functional assessment methods (FAMs) were identified. The most frequently used method was instrumented gait/motion analysis, followed by the Timed-Up-and-Go test (TUG), 6 minute walk test, timed stair climbing test, and various strength tests. These assessments were characterized by their diagnostic precision and applicability to daily activities. Wearables were frequently used, offering cost-effectiveness and remote monitoring benefits. However, their accuracy and potential discomfort for patients must be considered. Conclusion. The integration of objective functional assessments in THA presents promise as a progress-tracking modality for improving patient outcomes. Gait analysis and the TUG, along with advancing wearable sensor technology, have the potential to enhance patient care, surgical planning, and rehabilitation. Cite this article: Bone Joint J 2024;106-B(8):764–774

Aims

Robotic arm-assisted surgery offers accurate and reproducible guidance in component positioning and assessment of soft-tissue tensioning during knee arthroplasty, but the feasibility and early outcomes when using this technology for revision surgery remain unknown. The objective of this study was to compare the outcomes of robotic arm-assisted revision of unicompartmental knee arthroplasty (UKA) to total knee arthroplasty (TKA) versus primary robotic arm-assisted TKA at short-term follow-up.

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

Intra-articular (IA) injection may be used when treating hip osteoarthritis (OA). Common injections include steroids, hyaluronic acid (HA), local anaesthetic, and platelet-rich plasma (PRP). Network meta-analysis allows for comparisons between two or more treatment groups and uses direct and indirect comparisons between interventions. This network meta-analysis aims to compare the efficacy of various IA injections used in the management of hip OA with a follow-up of up to six months.

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This study compares the re-revision rate and mortality following septic and aseptic revision hip arthroplasty (rTHA) in registry data, and compares the outcomes to previously reported data.