Background. National hip fracture programmes are becoming widespread, but this practice is nascent and varied. The Scottish Hip Fracture Audit (SHFA) was an early adopter of this strategy and is credited with substantial systemic improvements in quality and outcomes. Objectives. To provide evidence and incentive to clinicians and administrators to adopt successful improvement strategies, and to facilitate data-driven change hip fracture care. Study Design and Methods. We reviewed the practice of seven national hip fracture improvement programmes in: Sweden, Denmark, Norway, Australia, New Zealand, UK, Scotland, and Ireland. We report our experience from the SHFA and describe: the results of our programme; challenges and learning points encountered, and successful strategies for implementing change. Results. There is variance in approach to data collection and reporting, for example: standalone programmes versus combined trauma and arthroplasty registries; annual trend reporting versus ‘snapshot’ or real-time information; population-level versus patient-level data, and the emphasis placed on service-level characteristics. The governance model also varies – some act as a passive data registry whereas others act as active agents of change and regulation. There is consensus on the key performance makers: prompt admission; early surgery and mobilisation, and a multidisciplinary approach. There have been significant challenges encountered by the SHFA with respect to funding, logistical, and political issues. Analysis of the effects of our programme have demonstrated its clinical efficacy, and has identified successful strategies for improvement. We describe this experience. Conclusions. The establishment of national audit programmes has resulted in significant improvements in quality, efficiency, and outcomes. This study of major national programmes provides evidence, incentive, and instruction to clinicians and administrators who seek to improve healthcare systems

Aims

National hip fracture registries audit similar aspects of care but there is variation in the actual data collected; these differences restrict international comparison, benchmarking, and research. The Fragility Fracture Network (FFN) published a revised minimum common dataset (MCD) in 2022 to improve consistency and interoperability. Our aim was to assess compatibility of existing registries with the MCD.

Abstract. Objectives. Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics. Methods. Data was curated through calls to members of the OATech Network+ (. https://www.oatechnetwork.org/. ). The requirements were 3D motion capture data from previous studies that related to analysing the biomechanics of knee OA, including participants with OA at any stage of progression plus healthy controls. As a minimum we required kinematic data of the lower limbs, plus associated kinetic data (i.e. ground reaction forces). Any additional, complementary data such as EMG could also be provided. Relevant ethical approvals had to be in place that allowed re-use of the data for other research purposes. The datasets were uploaded to a University hosted cloud platform. The database platform was developed using Javascript and hosted on a Windows server, located and managed within the department. Results. Three independent datasets were curated following the call to OATech Network+ members. These originated from separate studies collected from biomechanics labs at Cardiff University, Keele University, and Imperial College London. Participants with knee OA were at various stages of progression and all datasets included healthy controls. The total sample size of the three datasets is n=244, split approximately equally between healthy and knee OA participants. Naming conventions and formatting of the exported data varied greatly across datasets. Datasets were therefore formatted into a common format prior to upload, with guidelines developed for future contributions. Uploading data at the marker set level was too complicated for combination at the prototype stage. Therefore, processed variables relating to joint angles and joint moments were used. The resulting prototype database included an import function to align and standardise variables. A a simple query tool was further developed to extract outputs from the database, along with a suitable user interface for basic data exploration. Conclusion. Combining biomechanics dataset presents a wide range of challenges from both a technical and data governance context. Here we have taken the first steps to demonstrate a proof-of-concept that can combine heterogenous data from independent OA-related biomechanics studies into a combined, searchable resource. Expanding this in the future to a fully open access database will create an essential resource that will facilitate the application of data-driven models and analyses for better understanding, stratification and prediction of OA progression. Declaration of Interest. (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project

Purpose. Total and partial joint arthroplasty has been clinically proven to successfully relieve pain and improve function in patients with hip and knee degenerative arthrosis. It has been shown that early return to ambulation correlates well with functional scores. Moreover, the benefits of reduced narcotic use are multi-fold and range from reduced risk of addiction, gastrointenstinal and cardiopulmonary side effects. Establishing realistic pre-operative expectations regarding functional improvement and pain control will nevertheless impact patient satisfaction. Thus, the purpose of this study was to establish safe, achievable and data-driven benchmarks for post-operative ambulation and pain control in patients undergoing elective total joint arthroplasty. Methods. A retrospective chart review was performed for of all patients who underwent elective primary total or partial joint replacement by a single fellowship trained academic joint replacement surgeon from 2/1/2015 to 2/29/16. Patients under the age of 18 as well as those who underwent arthroplasty to treat a traumatic injury or to revise an arthroplasty were excluded. Charts were reviewed for the method of ambulation (no ambulation, wheelchair, assistive devices, no devices) and use of prescription pain medications by 6 weeks post-operatively. Results. 82 patient undergoing elective primary arthroplasty were identified and included in the analysis. 38% of patients underwent TKA (31 of 82) and 62% of patients (51 of 82) underwent THA via the direct anterior approach. Post-operatively 63% (52 of 82) patients were ambulating without assist device. An additional 24% (20 of 82) of patients were ambulating with a single point assist device by 6 weeks. 77% (63 of 82) of patients were pain free by 6 weeks post operatively and denied taking any prescription pain medications. Conclusions. Our analysis demonstrates that approximately 80% of patients undergoing elective total joint arthroplasty can be pain free without a need for prescription pain medication by 6 weeks post operatively. Moreover, while close to 90% of patients can expect to ambulate with at most, a single point assist, the majority (62%) can reliably expect to ambulate freely just 6 weeks after surgery. These results aid our understanding of post-operative return to function, and pain control. What is more, these data can inform a clinician in setting realistic, data-driven goals and expectations with patients undergoing elective total joint arthroplasty, thereby ultimately improving patient satisfaction with these commonly performed procedures

During OA the homeostasis of healthy articular chondrocytes is dysregulated, which leads to a phenotypical transition of the cells, further influenced by external stimuli. Chondrocytes sense those stimuli, integrate them at the intracellular level and respond by modifying their secretory and molecular state. This process is controlled by a complex interplay of intracellular factors. Each factor is influenced by a myriad of feedback mechanisms, making the prediction of what will happen in case of external perturbation challenging. Hampering the hypertrophic phenotype has emerged as a potential therapeutic strategy to help OA patients (Ripmeester et al. 2018). Therefore, we developed a computational model of the chondrocyte's underlying regulatory network (RN) to identify key regulators as potential drug targets. A mechanistic mathematical model of articular chondrocyte differentiation was implemented with a semi-quantitative formalism. It is composed of a protein RN and a gene RN(GRN) and developed by combining two strategies. First, we established a mechanistic network based on accumulation of decades of biological knowledge. Second, we combined that mechanistic network with data-driven modelling by inferring an OA-GRN using an ensemble of machine learning methods. This required a large gene expression dataset, provided by distinct public microarrays merged through an in-house pipeline for cross-platform integration. We successfully merged various micro-array experiments into one single dataset where the biological variance was predominant over the batch effect from the different technical platforms. The gain of information provided by this merge enabled us to reconstruct an OA-GRN which subsequently served to complete our mechanistic model. With this model, we studied the system's multi-stability, equating the model's stable states to chondrocyte phenotypes. The network structure explained the occurrence of two biologically relevant phenotypes: a hypertrophic-like and a healthy-like phenotype, recognized based on known cell state markers. Second, we tested several hypotheses that could trigger the onset of OA to validate the model with relevant biological phenomena. For instance, forced inflammation pushed the chondrocyte towards hypertrophy but this was partly rescued by higher levels of TGF-β. However, we could annihilate this rescue by concomitantly mimicking an increase in the ALK1/ALK5 balance. Finally, we performed a screening of in-silico (combinatorial) perturbations (inhibitions and/or over-activations) to identify key molecular factors involved in the stability of the chondrocyte state. More precisely, we looked for the most potent conditions for decreasing hypertrophy. Preliminary validation experiments have confirmed that PKA activation could decrease the hypertrophic phenotype in primary chondrocytes. Importantly the in-silico results highlighted that targeting two factors at the same time would greatly help reducing hypertrophic changes. A priori testing of conditions with in-silico models may cut time and cost of experiments via target prioritization and opens new routes for OA combinatorial therapies

Aims. Clinical decision support systems (CDSS) can support clinicians in selecting appropriate treatments for patients. The objective of this study was to examine if triaging patients with LBP to the most optimal treatment can be improved by using a data-driven approach with the help of machine learning as base of such a CDSS. Methods. A clinical database of the Groningen Spine Center containing patient-reported data from 1546 patients with LBP was used. From this dataset, a training dataset with 354 features was labeled on eight different treatments actually received by these patients. With this dataset, models were trained. A test dataset with 50 cases judged on treatments by 4 experts in LBP triage was used to test these models with data not used to train the models. Prediction accuracy and average area under curve (AUC) were used as performance measures for the models. Results. The AUC values indicated small to medium learning effects showing that machine learning on patient-reported data, to model decision-making processes on treatments for LBP, may be possible. One of the best performing models was the Bayesian Network (BN) model; e.g. predicted surgery with accuracy 0.78 (95% C.I. 0.68– 0.87) and AUC 0.70. Conclusion. Benefits to using BNs compared to other supervised machine learning techniques are that it is easy to exploit expert knowledge in BN models, meaning that advices generated by the model can be explained. The next step is to improve the BN accuracy so that it can actually be used in a CDSS. No conflicts of interest. Sources of funding: This work is partly funded by a grant from the Netherlands Organization for Health Research and Development (ZonMw), grant 10-10400-98-009

Introduction. Over the past few decades, opioid abuse has become a major threat to public health. In 2013 alone, enough opioid prescriptions were written in the United States for every American adult to have their own bottle of pills. Since then, opioid prescribing rates and opioid related deaths have continued to grow, with over 46 people dying on average each day from prescription opioid overdoses in 2016. Orthopaedic surgeons are among the top 5 specialties in the number of opioid prescriptions written. For many common surgeries, such as total knee arthroplasty (TKA), post-discharge prescriptions are based on prescriber habits and opinion. There exists limited data-driven protocols to guide post-operative opioid prescribing practices. The purpose of this prospective study was to determine the average postoperative opioid consumption in patients undergoing primary TKA using a novel mobile text messaging platform. We hypothesized that majority of patients undergoing TKA do not properly dispose of left over pills after surgery. Methods. 95 patients undergoing primary unilateral TKA with one of nine arthroplasty surgeons at a single orthopaedic specialty hospital were prospectively enrolled. Daily pain levels and opioid consumption, and quantity and disposal patterns for left over medications were collected for six weeks following surgery using a novel mobile phone text messaging system. This system automatically queried patients twice a day, storing responses on a secure third-party host that investigators monitored and used to generate data reports in real-time. Results. Of the 95 patients enrolled, 1 was excluded for undergoing a secondary procedure in the acute postoperative data collection period. Of the remaining 94 patients, 88 (93.6%) completed the daily SMS surveys and were included in analysis. Average age was 62.0 ± 7.1 years, BMI 31.1 ± 6.2, and length of hospital stay 2.6 ± 1.2 days. The average oral morphine equivalents (OME) consumed during the 6 weeks post-surgery were 617.3 ± 350.26 mg, which corresponds to a mean consumption of 82 oxycodone 5 mg tablets per patient. For the 55(58.5%) of patients who had stopped taking opioids within 6 weeks of surgery, there were on average 196.2 mg OME left over, the equivalent of 26 oxycodone 5 mg tablets; 20% of those who stopped taking opioids had at least 367.5 mg OME left over, the equivalent of at least 49 oxycodone 5 mg tablets. Only 26.5% of patients with left over pills described an appropriate method of disposal of left over medications, with most patients storing left over pills in their homes. Conclusion. This prospectively collected data provides a benchmark for average opioid consumption after uncomplicated primary unilateral TKA. Many patients are prescribed more opioids than they require, and left-over medication is infrequently disposed of appropriately, which increases the risk for illicit diversion. Prospective collection of patient reported opioid consumption has tremendous value in the development of national postoperative guideline protocols while informing further research and strategies aimed to reduce opioid use and misuse and in predicting patient characteristics associated with increased opioid consumption after surgery

Aims

To develop prediction models using machine-learning (ML) algorithms for 90-day and one-year mortality prediction in femoral neck fracture (FNF) patients aged 50 years or older based on the Hip fracture Evaluation with Alternatives of Total Hip arthroplasty versus Hemiarthroplasty (HEALTH) and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trials.

Initial treatment of traumatic spinal cord injury remains as controversial in 2023 as it was in the early 19th century, when Sir Astley Cooper and Sir Charles Bell debated the merits or otherwise of surgery to relieve cord compression. There has been a lack of high-class evidence for early surgery, despite which expeditious intervention has become the surgical norm. This evidence deficit has been progressively addressed in the last decade and more modern statistical methods have been used to clarify some of the issues, which is demonstrated by the results of the SCI-POEM trial. However, there has never been a properly conducted trial of surgery versus active conservative care. As a result, it is still not known whether early surgery or active physiological management of the unstable injured spinal cord offers the better chance for recovery. Surgeons who care for patients with traumatic spinal cord injuries in the acute setting should be aware of the arguments on all sides of the debate, a summary of which this annotation presents.

Cite this article: Bone Joint J 2023;105-B(4):347–355.

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

A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance.

Introduction Presently, many instruments exist for assessing both patient and surgeon-based satisfaction after joint replacement, including both generic and disease specific measures. Our aim was to derive and assess the validity of a reduced function scale of the WOMAC for patients with osteoarthritis of the hip and knee. Methods All unilateral data from 12 centres world-wide (UK, US, Canada and Australia) involved in an international, multi-centre outcome study for patients undergoing TKR were included for analysis. The reduced scale was derived from pre-operative and three month postoperative data using a combination of data-driven analysis and purely clinical methods. The reduced WOMAC was then extensively validated in three key areas; validity, reliability and responsiveness using 12 month post-operative data from the study and data from the Medicare Hip Replacement Study. Data from 898 patients pre-operatively and 806 patients at three months was used for the data driven section of analysis. For the clinical section, 30 members of the orthopaedic community were surveyed as to their opinions of which items should be retained in the reduced version of the scale. These results were then combined to produce a reduced function scale of seven items to be used in conjunction with the five item pain scale. This reduced scale was then scrutinised to ensure it’s validity (both construct and content), reliability (both internal consistency and reproducibility) and responsiveness (using Standardised Response Means). Results The items retained were: ascending stairs, rising from sitting, getting in/out of car, going shopping, rising from bed, taking off socks and sitting. The scales’s construct validity was confirmed by significant positive correlation with the SF-36 physical component score, the knee society function score, the Oxford knee score, and for the hip data, the Harris Hip Score and SF-12 physical component score. Cronbach’s alpha was consistently high (a> 0.85) with the reduced scale, showing it to be reliable. Conclusions The SRM’s indicated that the reduced scale may even be better at detecting change than the full scale

Aims

This is a multicentre, prospective assessment of a proportion of the overall orthopaedic trauma caseload of the UK. It investigates theatre capacity, cancellations, and time to surgery in a group of hospitals that is representative of the wider population. It identifies barriers to effective practice and will inform system improvements.

Introduction and Aims: Practice standards vary considerably for prophylactic pinning the contralateral hip opposite a scfe. This work provides a data-driven framework with which to analyse the risks, benefits, and costs of two modes of treatment: prophylactic pinning the normal side contralateral to a scfe versus observation; and pinning of subsequent slips. Method: A decision analysis model was constructed using the English language literature to estimate SCFE incidence and severity. The model framework assumes that if a hip is pinned prophylactically it will not slip. The benefits of prophylactic pinning are therefore determined by identifying the percentage of contralateral hips that will subsequently slip and then develop early osteoarthritis requiring total hip arthroplasty at a young age. In our cost analysis model, the cost of diagnosis, treatment, and follow-up was developed for the two treatment modes using actual hospital costs and standardised medicare reimbursement schedules for professional fees. Results: If a hip is not pinned prophylactically, there is a 7% risk of requiring a total hip arthroplasty in the contralateral hip at an early age due to osteoarthritis. This 7% is a combination of patients whose contralateral femoral epiphysis slipped moderately or severely and was pinned in a non-anatomic position (1.5% of the initial population) and patients whose substantial slip was not detected (5.5%). The risk of prophylactic pinning appears to be associated with a 0.3% chance of developing avascular necrosis. Other risks would include chances of infection or chondrolysis, although these have not been reported to date. If every patient is managed by the prophylactic pinning protocol, the total cost per patient, not including lost time at work or school, for pinning a slipped capital femoral epiphysis, prophylactically pinning the contra lateral side, and post-operative follow-up is $6266. Conversely, the total cost per patient for the second mode, pinning a slipped capital femoral epiphysis, post-operative follow-up and pinning of subsequent contralateral slips that are detected is $6864. Costs of later hip arthroplasty were not included. This analysis suggests that costs to a national health care system for treatment of SCFE would not increase by prophylactic contralateral pinning. Conclusions: Prophylactic pinning of the contralateral side is merited on the basis of both risk/benefit and cost-effectiveness analyses. Ultimately, the decision must incorporate the physician’s assessment of patient risk factors including: age, weight, co-morbidities, sports activities, the likelihood and feasibility of regular follow-up, and the patient’s and parents’ acceptance of prophylactic surgery

Presently, many instruments exist for assessing both patient - and surgeon-based satisfaction after joint replacement, including both generic (measures of general health status) and disease specific measures. As such, the US PORT study (1995) recommends use of both the WOMAC and SF-36. However, this means that studies need to incorporate at least these two lengthy questionnaires into protocols, which increases the pressure on patients for both time and difficulty, but also introduces some duplication of data. The SF-36 has been successfully reduced and validated to a 12 item questionnaire (SF-12) which can be used as a summarised generic health score. It would be of great benefit if a reduced version of the WOMAC could be derived to give a similar summarised disease-specific measurement tool. To derive and assess the validity of a reduced function scale of the WOMAC for patients with osteoarthritis of the hip and knee. All unilateral data from 12 centres world-wide (UK US Canada and Australia) involved in an international, multi-centre outcome study for patients undergoing TKR were included for analysis. The reduced scale was derived from pre-op and 3 month post op data using a combination of data-driven analysis and purely clinical methods. The reduced WOMAC was then extensively validated in three key areas; validity, reliability and responsiveness using 12 month post-op data from the study and data from the Medicare Hip Replacement Study. Data from 898 patients pre-operatively and 806 patients at 3-months were used for the data driven section of analysis. For the clinical section, 30 members of the orthopaedic community were surveyed as to their opinions of which items should be retained in the reduced version of the scale. These results were then combined to produce a reduced function scale of 7 items to be used in conjunction with the 5-item pain scale. The questions remaining in the scale (and their original number in the scale) were: 2) ascending stairs, 3) rising from sitting, 6) walking on flat, 7) getting in/out of car, 9) putting on socks/stockings, 10) rising from bed and 14) sitting. This reduced scale was then scrutinised to ensure it’s validity (both construct and content), reliability (both internal consistency and reproducibility) and responsiveness (using Standardised Response Means). When examining 12 month data the reduced scale compared favourably with the full scale both overall, and when sub-divided by age, sex and country. It’s construct validity was confirmed by significant positive correlation with the SF-36 physical component score, the knee society function score, the Oxford knee score, and for the hip data, the Harris hip score and SF-12 physical component score. Cronbach’s alpha was consistently high (α> 0.85) with the reduced scale, showing it to be reliable, and the SRM’s indicated that the reduced scale may even be better at detecting change than the full scale. This reduced WOMAC has been successfully derived and validated for use as a summarised and more practical version of the full WOMAC scale

Aims

The aim of this study was to explore current use of the Global Fragility Fracture Network (FFN) Minimum Common Dataset (MCD) within established national hip fracture registries, and to propose a revised MCD to enable international benchmarking for hip fracture care.

Aims

The COVID-19 pandemic presents an unprecedented burden on global healthcare systems, and existing infrastructures must adapt and evolve to meet the challenge. With health systems reliant on the health of their workforce, the importance of protection against disease transmission in healthcare workers (HCWs) is clear. This study collated responses from several countries, provided by clinicians familiar with practice in each location, to identify areas of best practice and policy so as to build consensus of those measures that might reduce the risk of transmission of COVID-19 to HCWs at work.