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. 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.Aims
Methods
Current levels of hip fracture morbidity contribute greatly to the overall burden on health and social care services. Given the anticipated ageing of the population over the coming decade, there is potential for this burden to increase further, although the exact scale of impact has not been identified in contemporary literature. We therefore set out to predict the future incidence of hip fracture and help inform appropriate service provision to maintain an adequate standard of care. Historical data from the Scottish Hip Fracture Audit (2017 to 2021) were used to identify monthly incidence rates. Established time series forecasting techniques (Exponential Smoothing and Autoregressive Integrated Moving Average) were then used to predict the annual number of hip fractures from 2022 to 2029, including adjustment for predicted changes in national population demographics. Predicted differences in service-level outcomes (length of stay and discharge destination) were analyzed, including the associated financial cost of any changes.Aims
Methods
Surgery is often delayed in patients who sustain a hip fracture and are treated with a total hip arthroplasty (THA), in order to await appropriate surgical expertise. There are established links between delay and poorer outcomes in all patients with a hip fracture, but there is little information about the impact of delay in the less frail patients who undergo THA. The aim of this study was to investigate the influence of delayed surgery on outcomes in these patients. A retrospective cohort study was undertaken using data from the Scottish Hip Fracture Audit between May 2016 and December 2020. Only patients undergoing THA were included, with categorization according to surgical treatment within 36 hours of admission (≤ 36 hours = ‘acute group’ vs > 36 hours = ‘delayed’ group). Those with delays due to being “medically unfit” were excluded. The primary outcome measure was 30-day survival. Costs were estimated in relation to the differences in the lengths of stay.Aims
Methods
The aim of this study was to examine perioperative blood transfusion practice, and associations with clinical outcomes, in a national cohort of hip fracture patients. A retrospective cohort study was undertaken using linked data from the Scottish Hip Fracture Audit and the Scottish National Blood Transfusion Service between May 2016 and December 2020. All patients aged ≥ 50 years admitted to a Scottish hospital with a hip fracture were included. Assessment of the factors independently associated with red blood cell transfusion (RBCT) during admission was performed, alongside determination of the association between RBCT and hip fracture outcomes.Aims
Methods
There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article:
The aim of this study was to determine the impact of hospital-level service characteristics on hip fracture outcomes and quality of care processes measures. This was a retrospective analysis of publicly available audit data obtained from the National Hip Fracture Database (NHFD) 2018 benchmark summary and Facilities Survey. Data extraction was performed using a dedicated proforma to identify relevant hospital-level care process and outcome variables for inclusion. The primary outcome measure was adjusted 30-day mortality rate. A random forest-based multivariate imputation by chained equation (MICE) algorithm was used for missing value imputation. Univariable analysis for each hospital level factor was performed using a combination of Tobit regression, Siegal non-parametric linear regression, and Mann-Whitney U test analyses, dependent on the data type. In all analyses, a p-value < 0.05 denoted statistical significance.Aims
Methods
The primary aim was to assess the independent influence of coronavirus disease (COVID-19) on 30-day mortality for patients with a hip fracture. The secondary aims were to determine whether: 1) there were clinical predictors of COVID-19 status; and 2) whether social lockdown influenced the incidence and epidemiology of hip fractures. A national multicentre retrospective study was conducted of all patients presenting to six trauma centres or units with a hip fracture over a 46-day period (23 days pre- and 23 days post-lockdown). Patient demographics, type of residence, place of injury, presentation blood tests, Nottingham Hip Fracture Score, time to surgery, operation, American Society of Anesthesiologists (ASA) grade, anaesthetic, length of stay, COVID-19 status, and 30-day mortality were recorded.Aims
Methods
We set out to determine if there is a difference in perioperative outcomes between early and delayed surgery in paediatric supracondylar humeral fractures in the absence of vascular compromise through a systematic review and meta-analysis. A literature search was performed, with search outputs screened for studies meeting the inclusion criteria. The groups of early surgery (ES) and delayed surgery (DS) were classified by study authors. The primary outcome measure was open reduction requirement. Meta-analysis was performed in the presence of sufficient study homogeneity. Individual study risk of bias was assessed using the Risk of Bias in Non-Randomised Studies – of Interventions (ROBINS-I) criteria, with the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) criteria used to evaluate outcomes independently.Aims
Materials and Methods
