The aim of this study was to evaluate the epidemiology and treatment of Perthes’ disease of the hip. This was an anonymized comprehensive cohort study of Perthes’ disease, with a nested consented cohort. A total of 143 of 144 hospitals treating children’s hip disease in the UK participated over an 18-month period. Cases were cross-checked using a secondary independent reporting network of trainee surgeons to minimize those missing. Clinician-reported outcomes were collected until two years. Patient-reported outcome measures (PROMs) were collected for a subset of participants.Aims
Methods
The aim of this study was to assess the ability of morphological spinal parameters to predict the outcome of bracing in patients with adolescent idiopathic scoliosis (AIS) and to establish a novel supine correction index (SCI) for guiding bracing treatment. Patients with AIS to be treated by bracing were prospectively recruited between December 2016 and 2018, and were followed until brace removal. In all, 207 patients with a mean age at recruitment of 12.8 years (SD 1.2) were enrolled. Cobb angles, supine flexibility, and the rate of in-brace correction were measured and used to predict curve progression at the end of follow-up. The SCI was defined as the ratio between correction rate and flexibility. Receiver operating characteristic (ROC) curve analysis was carried out to assess the optimal thresholds for flexibility, correction rate, and SCI in predicting a higher risk of progression, defined by a change in Cobb angle of ≥ 5° or the need for surgery.Aims
Methods
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
The Forgotten Joint Score-12 (FJS-12) is a validated patient-reported outcome measure (PROM) tool designed to assess artificial prosthesis awareness during daily activities following total hip arthroplasty (THA). The patient-acceptable symptom state (PASS) is the minimum cut-off value that corresponds to a patient’s satisfactory state-of-health. Despite the validity and reliability of the FJS-12 having been previously demonstrated, the PASS has yet to be clearly defined. This study aims to define the PASS of the FJS-12 following primary THA. We retrospectively reviewed all patients who underwent primary elective THA from 2019 to 2020, and answered both the FJS-12 and the Hip Disability and Osteoarthritis Outcome Score, Joint Replacement (HOOS, JR) questionnaires one-year postoperatively. HOOS, JR score was used as the anchor to estimate the PASS of FJS-12. Two statistical methods were employed: the receiver operating characteristic (ROC) curve point, which maximized the Youden index; and 75th percentile of the cumulative percentage curve of patients who had the HOOS, JR score difference larger than the cut-off value.Aims
Methods
Fungal and mycobacterial periprosthetic joint infections (PJI) are rare events. Clinicians are wary of missing these diagnoses, often leading to the routine ordering of fungal and mycobacterial cultures on periprosthetic specimens. Our goal was to examine the utility of these cultures and explore a modern bacterial culture technique using bacterial blood culture bottles (BCBs) as an alternative. We performed a retrospective review of patients diagnosed with hip or knee PJI between 1 January 2010 and 31 December 2019, at the Mayo Clinic in Rochester, Minnesota, USA. We included patients aged 18 years or older who had fungal, mycobacterial, or both cultures performed together with bacterial cultures. Cases with positive fungal or mycobacterial cultures were reviewed using the electronic medical record to classify the microbiological findings as representing true infection or not.Aims
Methods
To identify the minimum set of outcomes that should be collected in clinical practice and reported in research related to the care of children with idiopathic congenital talipes equinovarus (CTEV). A list of outcome measurement tools (OMTs) was obtained from the literature through a systematic review. Further outcomes were collected from patients and families through a questionnaire and interview process. The combined list, as well as the appropriate follow-up timepoint, was rated for importance in a two-round Delphi process that included an international group of orthopaedic surgeons, physiotherapists, nurse practitioners, patients, and families. Outcomes that reached no consensus during the Delphi process were further discussed and scored for inclusion/exclusion in a final consensus meeting involving international stakeholder representatives of practitioners, families, and patient charities.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:
A multicentre, randomized, clinician-led, pragmatic, parallel-group orthopaedic trial of two surgical procedures was set up to obtain high-quality evidence of effectiveness. However, the trial faced recruitment challenges and struggled to maintain recruitment rates over 30%, although this is not unusual for surgical trials. We conducted a qualitative study with the aim of gathering information about recruitment practices to identify barriers to patient consent and participation to an orthopaedic trial. We collected 11 audio recordings of recruitment appointments and interviews of research team members (principal investigators and research nurses) from five hospitals involved in recruitment to an orthopaedic trial. We analyzed the qualitative data sets thematically with the aim of identifying aspects of informed consent and information provision that was either unclear, disrupted, or hindered trial recruitment.Aims
Methods
COVID-19 has compounded a growing waiting list problem, with over 4.5 million patients now waiting for planned elective care in the UK. Views of patients on waiting lists are rarely considered in prioritization. Our primary aim was to understand how to support patients on waiting lists by hearing their experiences, concerns, and expectations. The secondary aim was to capture objective change in disability and coping mechanisms. A minimum representative sample of 824 patients was required for quantitative analysis to provide a 3% margin of error. Sampling was stratified by body region (upper/lower limb, spine) and duration on the waiting list. Questionnaires were sent to a random sample of elective orthopaedic waiting list patients with their planned intervention paused due to COVID-19. Analyzed parameters included baseline health, change in physical/mental health status, challenges and coping strategies, preferences/concerns regarding treatment, and objective quality of life (EuroQol five-dimension questionnaire (EQ-5D), Generalized Anxiety Disorder 2-item scale (GAD-2)). Qualitative analysis was performed via the Normalization Process Theory.Aims
Methods
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
Aims. People with severe, persistent low back pain (LBP) may be offered lumbar spine fusion surgery if they have had insufficient benefit from recommended non-surgical treatments. However, National Institute for Health and Care Excellence (NICE) 2016 guidelines recommended not offering spinal fusion surgery for adults with LBP, except as part of a randomized clinical trial. This survey aims to describe UK
Aims. The aim of this study was to produce clinical consensus recommendations about the non-surgical treatment of children with Perthes’ disease. The recommendations are intended to support clinical practice in a condition for which there is no robust evidence to guide optimal care. Methods. A two-round, modified Delphi study was conducted online. An advisory group of children’s orthopaedic specialists consisting of physiotherapists, surgeons, and clinical nurse specialists designed a survey. In the first round, participants also had the opportunity to suggest new statements. The survey included statements related to ‘Exercises’, ‘Physical activity’, ‘Education/information sharing’, ‘Input from other services’, and ‘Monitoring assessments’. The survey was shared with
Aims. Advances in treatment have extended the life expectancy of patients with metastatic bone disease (MBD). Patients could experience more skeletal-related events (SREs) as a result of this progress. Those who have already experienced a SRE could encounter another local management for a subsequent SRE, which is not part of the treatment for the initial SRE. However, there is a noted gap in research on the rate and characteristics of subsequent SREs requiring further localized treatment, obligating
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
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,
