30-day emergency readmission is an indicator of treatment related complication once discharged, resulting in readmission. A board-approved quality improvement pathway was introduced to reduce elective re-admissions. The pathway involved telephone and email contact details provision to patients for any non-life threatening medical assistance, allowing for initial nurse led management of all issues. A new clinic room available 7 days, and same day ultrasound scanning for DVT studies were introduced. A capability, opportunity and behavior model of change was implemented. Readmission rates before and six months after implementation were collected from Model Hospital. A database used to document patient communications was interrogated for patient outcomes.Abstract
Introduction
Method
Over 8000 total hip arthroplasties (THA) in the UK were revised in 2019, half for aseptic loosening. It is believed that Artificial Intelligence (AI) could identify or predict failing THA and result in early recognition of poorly performing implants and reduce patient suffering. The aim of this study is to investigate whether Artificial Intelligence based machine learning (ML) / Deep Learning (DL) techniques can train an algorithm to identify and/or predict failing uncemented THA. Consent was sought from patients followed up in a single design, uncemented THA implant surveillance study (2010–2021). Oxford hip scores and radiographs were collected at yearly intervals. Radiographs were analysed by 3 observers for presence of markers of implant loosening/failure: periprosthetic lucency, cortical hypertrophy, and pedestal formation. DL using the RGB ResNet 18 model, with images entered chronologically, was trained according to revision status and radiographic features. Data augmentation and cross validation were used to increase the available training data, reduce bias, and improve verification of results. 184 patients consented to inclusion. 6 (3.2%) patients were revised for aseptic loosening. 2097 radiographs were analysed: 21 (11.4%) patients had three radiographic features of failure. 166 patients were used for ML algorithm testing of 3 scenarios to detect those who were revised. 1) The use of revision as an end point was associated with increased variability in accuracy. The area under the curve (AUC) was 23–97%. 2) Using 2/3 radiographic features associated with failure was associated with improved results, AUC: 75–100%. 3) Using 3/3 radiographic features, had less variability, reduced AUC of 73%, but 5/6 patients who had been revised were identified (total 66 identified). The best algorithm identified the greatest number of revised hips (5/6), predicting failure 2–8 years before revision, before all radiographic features were visible and before a significant fall in the Oxford Hip score. True-Positive: 0.77, False Positive: 0.29. ML algorithms can identify failing THA before visible features on radiographs or before PROM scores deteriorate. This is an important finding that could identify failing THA early.
In the UK, the NHS generates an estimated 25 megatonnes of carbon dioxide equivalents (4% to 5% of the nation’s total carbon emissions) and produces over 500,000 tonnes of waste annually. There is limited evidence demonstrating the principles of sustainability and its benefits within orthopaedic surgery. The primary aim of this study was to analyze the environmental impact of orthopaedic surgery and the environmentally sustainable initiatives undertaken to address this. The secondary aim of this study was to describe the barriers to making sustainable changes within orthopaedic surgery. A literature search was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines through EMBASE, Medline, and PubMed libraries using two domains of terms: “orthopaedic surgery” and “environmental sustainability”.Aims
Methods
Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy.Aims
Methods
The aims of our systematic review were to assess the Nickel sensitising potential of total knee arthroplasty (TKA), explore the relationship between Nickel hypersensitivity and clinical outcomes, and evaluate the utility of skin patch testing pre- and/or post-operatively. A literature search was performed through EMBASE, Medline and PubMed databases. Articles were screened independently by two investigators. Levels of Evidence were assessed using Oxford CEBM criteria and quality was assessed using MINORS and Cochrane risk-of-bias tools.Abstract
Introduction
Methodology
The NHS generates 4–5% of the nation's total carbon emissions and over 500,000 tonnes of waste annually. Up to one-third of waste from orthopaedic procedures are recyclable, with large joint arthroplasties producing more than other subspecialties. However, there is limited evidence demonstrating the principles of sustainability and its benefits within orthopaedic surgery. This study aimed to analyse the environmental impact and sustainable initiatives undertaken within orthopaedic surgery. A systematic review was performed according to PRISMA guidelines. The systematic search was conducted through EMBASE, Medline, and PubMed libraries.Abstract
Introduction
Methodology
There is a trend towards minimising length of stay (LOS) after total knee arthroplasty (TKA), as longer LOS is associated with poorer outcomes and higher costs. Patient factors known to influence LOS after TKA include age and ASA grade. Evidence regarding body mass index (BMI) in particular is conflicting. Some studies find that increased BMI predicts greater LOS, while others find no such relationship. Previous studies have generally not examined socioeconomic status, which may be a confounder. They have generally been conducted outside the UK, and prior to the Covid-19 pandemic. We conducted a retrospective cohort study of 1031 primary TKAs performed 01-04-2021 to 31-12-2021, after resumption of elective surgery in our centre. A multivariate regression analysis was performed using a Poisson model over pre-operative variables (BMI, age, gender, ASA grade, index of multiple deprivation, and living arrangement) and peri-operative variables (AM/PM operation, operation side, duration, and day of the week).Abstract
Background
Methods
End-stage arthropathy is a well-known complication of haemophilia, with recurrent haemarthroses leading to joint destruction, deformity, pain, and stiffness. In the knee, this is often treated with total knee arthroplasty (TKA), which can be more challenging in patients with haemophilia (PwH) and associated with poorer outcomes. We conducted a systematic literature review and meta-analysis to determine implant survivorship, functional outcomes and complication rates. A systematic review was conducted using MEDLINE, EMBASE, and PubMed for studies reporting TKA outcomes with Kaplan-Meier survivorship in PwH (PROSPERO registered). Meta-analysis was performed for survivorship and outcomes, and the results were compared to outcomes from the National Joint Registry (NJR).Abstract
Aim
Method
The aims of this study were to assess mapping models to predict the three-level version of EuroQoL five-dimension utility index (EQ-5D-3L) from the Oxford Knee Score (OKS) and validate these before and after total knee arthroplasty (TKA). A retrospective cohort of 5,857 patients was used to create the prediction models, and a second cohort of 721 patients from a different centre was used to validate the models, all of whom underwent TKA. Patient characteristics, BMI, OKS, and EQ-5D-3L were collected preoperatively and one year postoperatively. Generalized linear regression was used to formulate the prediction models.Aims
Methods
There is little published on the outcomes after restarting elective orthopaedic procedures following cessation of surgery due to the COVID-19 pandemic. During the pandemic, the reported perioperative mortality in patients who acquired SARS-CoV-2 infection while undergoing elective orthopaedic surgery was 18% to 20%. The aim of this study is to report the surgical outcomes, complications, and risk of developing COVID-19 in 2,316 consecutive patients who underwent elective orthopaedic surgery in the latter part of 2020 and comparing it to the same, pre-pandemic, period in 2019. A retrospective service evaluation of patients who underwent elective surgical procedures between 16 June 2020 and 12 December 2020 was undertaken. The number and type of cases, demographic details, American society of Anesthesiologists (ASA) grade, BMI, 30-day readmission rates, mortality, and complications at one- and six-week intervals were obtained and compared with patients who underwent surgery during the same six-month period in 2019.Aims
Methods
A modular hemiarthroplasty has a Metal-on-Metal (MoM) taper-trunnion junction, which may lead to increased wear and Adverse-Reaction-to-Metal-Debris (ARMD). To-date no wear related issues have been described in the elderly and less active that receives a hemiarthroplasty. This study aims to determine in vivo wear (i.e. serum metal ion levels) in hip hemiarthroplasty, and identify factors associated with increased wear. This is a prospective, IRB approved, single-centre, cohort study of patients that received an uncemented, modular hemiarthroplasty of proven design for the treatment of hip fracture between 2013–2015. All, alive, patients at 12-months post-implantation with AMTS≥6 were invited to participate. Of the 125 eligible patients, 50 accepted the invitation and were reviewed, including clinical/radiological assessment, metal-ion ([Chromium (Cr) and Cobalt (Co)]) measurement and Oxford Hip Score (OHS). Acetabular erosion was graded (0–3: normal-protrusio). Metal ion levels were considered high if ≥7ppb. The mean OHS was 37 (SD: 10). No acetabular erosion was detected in 21, whilst the remaining had either grade-1 (n=21) or grade-2 (n=8). The median Cr and Co levels were 2.9 (SD:9) and 2.2 (SD:4) respectively. There were 8 cases (16%) with high ion levels. To-date only 2 of them has an ARMD lesion, and none have been revised. Patients with metal ion levels had similar pre-fall mobility, taper- and head- size and OHS to those with low metal ion levels (p=0.2–0.7) However, all hips with high metal ion levels had evidence of acetabular erosion (≥1). Modular Hip hemiarthroplasties and their taper-trunnion junction are not immune to high wear and ARMD despite being implanted in a less active cohort. Acetabular erosion should alert clinicians, as it is associated with 20× increased-risk of taper wear, presumably due to the increased transmitted torque. Whether the use of modular hemiarthroplasties should remain is debatable.
