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 clinicians are conscious of the medicolegal and ethical considerations surrounding the medium and adhere strictly to the relevant data protection legislation and storage framework. It remains to be seen how organizations harness the full spectrum of the technology to facilitate effective patient care. Cite this article: Bone Joint J 2023;105-B(8):843–849

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. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Aims. To identify unanswered questions about the prevention, diagnosis, treatment, and rehabilitation and delivery of care of first-time soft-tissue knee injuries (ligament injuries, patella dislocations, meniscal injuries, and articular cartilage) in children (aged 12 years and older) and adults. Methods. The James Lind Alliance (JLA) methodology for Priority Setting Partnerships was followed. An initial survey invited patients and healthcare professionals from the UK to submit any uncertainties regarding soft-tissue knee injury prevention, diagnosis, treatment, and rehabilitation and delivery of care. Over 1,000 questions were received. From these, 74 questions (identifying common concerns) were formulated and checked against the best available evidence. An interim survey was then conducted and 27 questions were taken forward to the final workshop, held in January 2023, where they were discussed, ranked, and scored in multiple rounds of prioritization. This was conducted by healthcare professionals, patients, and carers. Results. The top ten included questions regarding prevention, diagnosis, treatment, and rehabilitation. The number one question was, ‘How urgently do soft-tissue knee injuries need to be treated for the best outcome?’. This reflects the concerns of patients, carers, and the wider multidisciplinary team. Conclusion. This validated process has generated ten important priorities for future soft-tissue knee injury research. These have been submitted to the National Institute for Health and Care Research. All 27 questions in the final workshop have been published on the JLA website. Cite this article: Bone Joint J 2024;106-B(3):232–239

Aims. A review of the literature on elbow replacement found no consistency in the clinical outcome measures which are used to assess the effectiveness of interventions. The aim of this study was to define core outcome domains for elbow replacement. Methods. A real-time Delphi survey was conducted over four weeks using outcomes from a scoping review of 362 studies on elbow replacement published between January 1990 and February 2021. A total of 583 outcome descriptors were rationalized to 139 unique outcomes. The survey consisted of 139 outcomes divided into 18 domains. The readability and clarity of the survey was determined by an advisory group including a patient representative. Participants were able to view aggregated responses from other participants in real time and to revisit their responses as many times as they wished during the study period. Participants were able to propose additional items for inclusion. A Patient and Public Inclusion and Engagement (PPIE) panel considered the consensus findings. Results. A total of 45 respondents completed the survey. Nine core mandatory domains were identified: ‘return to work or normal daily role’; delivery of care was measured in the domains ‘patient satisfaction with the outcome of surgery’ and ‘would the patient have the same operation again’; ‘pain’; ‘revision’; ‘elbow function’; ‘independence in activities of daily living’; ‘health-related quality of life’; and ‘adverse events’. ‘Elbow range of motion’ was identified as important by consensus but was felt to be less relevant by the PPIE panel. The PPIE panel unanimously stated that pain should be used as the primary outcome domain. Conclusion. This study defined core domains for the clinical outcomes of elbow replacement obtained by consensus from patients, carers, and healthcare professionals. Pain may be used as the primary outcome in future studies, where appropriate. Further work is required to define the instruments that should be used. Cite this article: Bone Joint J 2024;106-B(11):xxx–xxx

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. The aim of this study was to review the provision of total elbow arthroplasties (TEAs) in England, including the incidence, the characteristics of the patients and the service providers, the types of implant, and the outcomes. Methods. We analyzed the primary TEAs recorded in the National Joint Registry (NJR) between April 2012 and December 2022, with mortality data from the Civil Registration of Deaths dataset. Linkage with Hospital Episode Statistics-Admitted Patient Care (HES-APC) data provided further information not collected by the NJR. The incidences were calculated using estimations of the populations from the Office for National Statistics. The annual number of TEAs performed by surgeons and hospitals was analyzed on a national and regional basis. Results. A total of 3,891 primary TEAs were included. The annual incidence of TEA was between 0.72 and 0.82 per 100,000 persons before 2020 and declined to 0.4 due to a decrease in elective TEAs during the COVID-19 pandemic, with a slight recovery in 2022. Older patients, those of white ethnicity and females, were more likely to undergo TEA. Those who underwent elective TEA had a median wait of between 89 (IQR 41 to 221) and 122 days (IQR 74 to 189) in the years before 2021, and this increased to 183 days (IQR 66 to 350) in 2021. The number of TEAs performed by surgeons per annum remained unchanged, with a median of two (IQR 1 to 3). The median annual number of TEAs per region was three to six times higher than the median annual case load of the highest volume hospital in a region. Patients in the lowest socioeconomic group had a higher rate of serious adverse events and mortality (11%) when undergoing TEA for acute trauma. Conclusion. In England, TEA is more common in older age groups, those of white ethnicity, and females. The COVID-19 pandemic affected the incidence of elective TEA and waiting times, and the provision of TEA has not yet recovered. The Getting it Right First Time recommendation of centralizing services to one centre per region could result in up to a six-fold increase in the number of TEAs being performed in some centres. Cite this article: Bone Joint J 2024;106-B(11):1312–1320

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. 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 clinicians and researchers can use to guide patient care and to stratify the analysis of quality improvement and research projects. Cite this article: Bone Joint J 2024;106-B(4):412–418

Aims. The James Lind Alliance aims to bring patients, carers, and clinicians together to identify uncertainties regarding care. A Priority Setting Partnership was established by the British Association for Surgery of the Knee in conjunction with the James Lind Alliance to identify research priorities related to the assessment, management, and rehabilitation of patients with persistent symptoms after knee arthroplasty. Methods. The project was conducted using the James Lind Alliance protocol. A steering group was convened including patients, surgeons, anaesthetists, nurses, physiotherapists, and researchers. Partner organizations were recruited. A survey was conducted on a national scale through which patients, carers, and healthcare professionals submitted key unanswered questions relating to problematic knee arthroplasties. These were analyzed, aggregated, and synthesized into summary questions and the relevant evidence was checked. After confirming that these were not answered in the current literature, 32 questions were taken forward to an interim prioritization survey. Data from this survey informed a shortlist taken to a final consensus meeting. Results. A total of 769 questions were received during the initial survey with national reach across the UK. These were refined into 32 unique questions by an independent information specialist. The interim prioritization survey was completed by 201 respondents and 25 questions were taken to a final consensus group meeting between patients, carers, and healthcare professionals. Consensus was reached for ranking the top ten questions for publication and dissemination. Conclusions. The top ten research priorities focused on pain, infection, stiffness, health service configuration, surgical and non-surgical management strategies, and outcome measures. This list will guide funders and help focus research efforts within the knee arthroplasty community. Cite this article: Bone Joint J 2020;102-B(9):1176–1182

Aims. Previous studies have reported an increased risk for postoperative complications in the Medicaid population undergoing total hip arthroplasty (THA). These studies have not controlled for the surgeon’s practice or patient care setting. This study aims to evaluate whether patient point of entry and Medicaid status plays a role in quality outcomes and discharge disposition following THA. Methods. The electronic medical record at our institution was retrospectively reviewed for all primary, unilateral THA between January 2016 and January 2018. THA recipients were categorized as either Medicaid or non-Medicaid patients based on a visit to our institution’s Hospital Ambulatory Care Center (HACC) within the six months prior to surgery. Only patients who had been operated on by surgeons (CML, JV, JDS, RS) with at least ten Medicaid and ten non-Medicaid patients were included in the study. The patients included in this study were 56.33% female, had a mean age of 60.85 years, and had a mean BMI of 29.14. The average length of follow-up was 343.73 days. Results. A total of 426 hips in 403 patients were included in this study, with 114 Medicaid patients and 312 non-Medicaid patients. Medicaid patients had a significantly lower mean age (54.68 years (SD 12.33) vs 63.10 years (SD 12.38); p < 0.001), more likely to be black or ‘other’ race (27.19% vs 13.46% black; 26.32% vs 12.82% other; p < 0.001), and more likely to be a current smoker (19.30% vs 9.29%; p = 0.001). After adjusting for patient risk factors, there was a significant Medicaid effect on length of stay (LOS) (rate ratio 1.129, 95% confidence interval (CI) 1.048 to 1.216; p = 0.001) and facility discharge (odds ratio 2.010, 95% CI 1.398 to 2.890; p < 0.001). There was no Medicaid effect on surgical time (exponentiated β coefficient 1.015, 95% CI 0.995 to 1.036; p = 0.136). There was no difference in 30-day readmission, 90-day readmission, 30-day infections, 90-day infections, and 90-day mortality between the two groups. Conclusion. After controlling for patient variables, there was a statistically significant Medicaid effect on LOS and facility discharge. These results indicate that clinically similar outcomes can be achieved for Medicaid patients; however, further work is needed on maximizing social support and preoperative patient education with a focus on successful home discharge. Cite this article: Bone Joint J 2020;102-B(7 Supple B):78–84

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

Benefits of early stabilization of femoral shaft fractures, in mitigation of pulmonary and other complications, have been recognized over the past decades. Investigation into the appropriate level of resuscitation, and other measures of readiness for definitive fixation, versus a damage control strategy have been ongoing. These principles are now being applied to fractures of the thoracolumbar spine, pelvis, and acetabulum. Systems of trauma care are evolving to encompass attention to expeditious and safe management of not only multiply injured patients with these major fractures, but also definitive care for hip and periprosthetic fractures, which pose a similar burden of patient recumbency until stabilized. Future directions regarding refinement of patient resuscitation, assessment, and treatment are anticipated, as is the potential for data sharing and registries in enhancing trauma system functionality.

Cite this article: Bone Joint J 2023;105-B(4):361–364.

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.

The subject of noise in the operating theatre was recognized as early as 1972 and has been compared to noise levels on a busy highway. While noise-induced hearing loss in orthopaedic surgery specifically has been recognized as early as the 1990s, it remains poorly studied. As a result, there has been renewed focus in this occupational hazard. Noise level is typically measured in decibels (dB), whereas noise adjusted for human perception uses A-weighted sound levels and is expressed in dBA. Mean operating theatre noise levels range between 51 and 75 dBA, with peak levels between 80 and 119 dBA. The greatest sources of noise emanate from powered surgical instruments, which can exceed levels as high as 140 dBA. Newer technology, such as robotic-assisted systems, contribute a potential new source of noise. This article is a narrative review of the deleterious effects of prolonged noise exposure, including noise-induced hearing loss in the operating theatre team and the patient, intraoperative miscommunication, and increased cognitive load and stress, all of which impact the surgical team’s overall performance. Interventions to mitigate the effects of noise exposure include the use of quieter surgical equipment, the implementation of sound-absorbing personal protective equipment, or changes in communication protocols. Future research endeavours should use advanced research methods and embrace technological innovations to proactively mitigate the effects of operating theatre noise.

Cite this article: Bone Joint J 2024;106-B(10):1039–1043.

Aims

The aim of this study was to compare the migration of the femoral component, five years postoperatively, between patients with a highly cross-linked polyethylene (HXLPE) insert and those with a conventional polyethylene (PE) insert in an uncemented Triathlon fixed insert cruciate-retaining total knee arthroplasty (TKA). Secondary aims included clinical outcomes and patient-reported outcome measures (PROMs). We have previously reported the migration and outcome of the tibial components in these patients.

The importance of registries has been brought into focus by recent UK national reports focusing on implant (Cumberlege) and surgeon (Paterson) performance. National arthroplasty registries provide real-time, real-world information about implant, hospital, and surgeon performance and allow case identification in the event of product recall or adverse surgical outcomes. They are a valuable resource for research and service improvement given the volume of data recorded and the longitunidal nature of data collection. This review discusses the current value of registry data as it relates to both clinical practice and research.

Cite this article: Bone Joint J 2023;105-B(4):356–360.

Aims

Implant failure has become more common as the number of primary total ankle arthroplasties (TAAs) performed has increased. Although revision arthroplasty has gained attention for functional preservation, the long-term results remain unclear. This study aimed to assess the long-term outcomes of revision TAA using a mobile-bearing prosthesis in a considerably large cohort; the risk factors for failure were also determined.

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.