Aims. For the increasing number of working-age patients undergoing total hip or total knee arthroplasty (THA/TKA), return to
Aims. The aim of this study was to identify predictors of return to
work (RTW) after revision lower limb arthroplasty in patients of
working age in the United Kingdom. Patients and Methods. We assessed 55 patients aged ≤ 65 years after revision total
hip arthroplasty (THA). There were 43 women and 12 men with a mean
age of 54 years (23 to 65). We also reviewed 30 patients after revision
total knee arthroplasty (TKA). There were 14 women and 16 men with
a mean age of 58 years (48 to 64). Preoperatively, age, gender,
body mass index, social deprivation, mode of failure, length of
primary implant survival,
Aims. To review the evidence and reach consensus on recommendations for follow-up after total hip and knee arthroplasty. Methods. A programme of
Aims. Total femoral arthroplasty (TFA) is a rare procedure used in cases of significant femoral bone loss, commonly from cancer, infection, and trauma. Low patient numbers have resulted in limited published
Aims. The extended wait that most patients are now experiencing for hip and knee arthroplasty has raised questions about whether reliance on waiting time as the primary driver for prioritization is ethical, and if other additional factors should be included in determining surgical priority. Our Prioritization of THose aWaiting hip and knee ArthroplastY (PATHWAY) project will explore which perioperative factors are important to consider when prioritizing those on the waiting list for hip and knee arthroplasty, and how these factors should be weighted. The final product will include a weighted benefit score that can be used to aid in surgical prioritization for those awaiting elective primary hip and knee arthroplasty. Methods. There will be two linked
Aims. Periprosthetic joint infection (PJI) demonstrates the most feared complication after total joint replacement (TJR). The current
The COVID-19 pandemic has disrupted the provision of arthroplasty services in England, Wales, and Northern Ireland. This study aimed to quantify the backlog, analyze national trends, and predict time to recovery. We performed an analysis of the mandatory prospective national registry of all independent and publicly funded hip, knee, shoulder, elbow, and ankle replacements in England, Wales, and Northern Ireland between January 2019 and December 2022 inclusive, totalling 729,642 operations. The deficit was calculated per year compared to a continuation of 2019 volume. Total deficit of cases between 2020 to 2022 was expressed as a percentage of 2019 volume. Sub-analyses were performed based on procedure type, country, and unit sector.Aims
Methods
The aim of this study was to evaluate the diagnostic accuracy of the absolute synovial polymorphonuclear neutrophil cell (PMN) count for the diagnosis or exclusion of periprosthetic joint infection (PJI) after total hip (THA) or knee arthroplasty (TKA). In this retrospective cohort study, 147 consecutive patients with acute or chronic complaints following THA and TKA were included. Diagnosis of PJI was established based on the 2018 International Consensus Meeting criteria. A total of 39 patients diagnosed with PJI (32 chronic and seven acute) and 108 patients with aseptic complications were surgically revised.Aims
Methods
While internet search engines have been the primary information source for patients’ questions, artificial intelligence large language models like ChatGPT are trending towards becoming the new primary source. The purpose of this study was to determine if ChatGPT can answer patient questions about total hip (THA) and knee arthroplasty (TKA) with consistent accuracy, comprehensiveness, and easy readability. We posed the 20 most Google-searched questions about THA and TKA, plus ten additional postoperative questions, to ChatGPT. Each question was asked twice to evaluate for consistency in quality. Following each response, we responded with, “Please explain so it is easier to understand,” to evaluate ChatGPT’s ability to reduce response reading grade level, measured as Flesch-Kincaid Grade Level (FKGL). Five resident physicians rated the 120 responses on 1 to 5 accuracy and comprehensiveness scales. Additionally, they answered a “yes” or “no” question regarding acceptability. Mean scores were calculated for each question, and responses were deemed acceptable if ≥ four raters answered “yes.”Aims
Methods
Arthroplasty has been shown to generate the most waste among all orthopaedic subspecialties, and it is estimated that hip and knee arthroplasty generate in excess of three million kg of waste annually in the UK. Infectious waste generates up to ten times more CO2 compared with recycled waste, and previous studies have shown that over 90% of waste in the infectious stream is misallocated. We assessed the effect of real-time waste segregation by an unscrubbed team member on waste generation in knee and hip arthroplasty cases, and compared this with a simple educational intervention during the ‘team brief’ at the start of the operating list across two sites. Waste was categorized into five categories: infectious, general, recycling, sharps, and linens. Each category was weighed at the end of each case using a digital weighing scale. At Site A (a tertiary orthopaedic hospital), pre-intervention data were collected for 16 total knee arthroplasy (TKA) and 15 total hip arthroplasty (THA) cases. Subsequently, for ten TKA and ten THA cases, an unscrubbed team member actively segregated waste in real-time into the correct streams. At Site B (a district general hospital), both pre- and post-intervention groups included ten TKA and ten THA cases. The intervention included reminding staff during the ‘team brief’ to segregate waste correctly.Aims
Methods
The COVID-19 pandemic has caused unprecedented disruption to elective orthopaedic services. The primary objective of this study was to examine changes in functional scores in patients awaiting total hip arthroplasty (THA), total knee arthroplasty (TKA), and unicompartmental knee arthroplasty (UKA). Secondary objectives were to investigate differences between these groups and identify those in a health state ‘worse than death’ (WTD). In this prospective cohort study, preoperative Oxford hip and knee scores (OHS/OKS) were recorded for patients added to a waiting list for THA, TKA, or UKA, during the initial eight months of the COVID-19 pandemic, and repeated at 14 months into the pandemic (mean interval nine months (SD 2.84)). EuroQoL five-dimension five-level health questionnaire (EQ-5D-5L) index scores were also calculated at this point in time, with a negative score representing a state WTD. OHS/OKS were analyzed over time and in relation to the EQ-5D-5L.Aims
Methods
This study aims to evaluate the impact of metabolic syndrome in the setting of obesity on in-hospital outcomes and resource use after total joint replacement (TJR). A retrospective analysis was conducted using the National Inpatient Sample from 2006 to the third quarter of 2015. Discharges representing patients aged 40 years and older with obesity (BMI > 30 kg/m2) who underwent primary TJR were included. Patients were stratified into two groups with and without metabolic syndrome. The inverse probability of treatment weighting (IPTW) method was used to balance covariates.Aims
Methods
The aim of this study was to evaluate the healthcare costs and benefits of enoxaparin compared to aspirin in the prevention of symptomatic venous thromboembolism (VTE) after total hip arthroplasty (THA) or total knee arthroplasty (TKA) using data from the CRISTAL trial. This trial-based economic analysis reports value for money as incremental cost per quality-adjusted life-year (QALY) gained in 2022 Australian dollars, compared to a single threshold value of AUD$70,000 per QALY. Event costs were estimated based on occurrence of VTEs and bleeds, and on published guidelines for treatment. Unit costs were taken from Australian sources. QALYs were estimated using CRISTAL six-month follow-up data. Sensitivity analyses are presented that vary the cost of VTE treatment, and extend the analyses to two years.Aims
Methods
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
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. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS).Aims
Methods
Aim. Restarting elective services presents a challenge to restore and improve many of the planned patient care pathways which have been suspended during the response to the COVID-19 pandemic. A significant backlog of planned elective
Aims. To establish the utility of adding the laboratory-based synovial alpha-defensin immunoassay to the traditional diagnostic work-up of a prosthetic joint infection (PJI). Methods. A group of four physicians evaluated 158 consecutive patients who were
Data of high quality are critical for the meaningful interpretation of registry information. The National Joint Registry (NJR) was established in 2002 as the result of an unexpectedly high failure rate of a cemented total hip arthroplasty. The NJR began data collection in 2003. In this study we report on the outcomes following the establishment of a formal data quality (DQ) audit process within the NJR, within which each patient episode entry is validated against the hospital unit’s Patient Administration System and vice-versa. This process enables bidirectional validation of every NJR entry and retrospective correction of any errors in the dataset. In 2014/15 baseline average compliance was 92.6% and this increased year-on-year with repeated audit cycles to 96.0% in 2018/19, with 76.4% of units achieving > 95% compliance. Following the closure of the audit cycle, an overall compliance rate of 97.9% was achieved for the 2018/19 period. An automated system was initiated in 2018 to reduce administrative burden and to integrate the DQ process into standard workflows. Our processes and quality improvement results demonstrate that DQ may be implemented successfully at national level, while minimizing the burden on hospitals. Cite this article:
The primary aim of this study was to assess whether patients waiting six months or more for a total hip (THA) or knee (KA) arthroplasty had a deterioration in their health-related quality of life (HRQoL). Secondary aims were to assess changes in frailty and the number of patients living in a state considered to be worse than death (WTD), and factors associated with changes in HRQoL and frailty. This cross-sectional study included 326 patients, 150 males (46.0%) and 176 females (54.0%), with a mean age of 68.6 years (SD 9.8) who were randomly selected from waiting lists at four centres and had been waiting for six months or more (median 13 months, interquartile range 10 to 21) for a primary THA (n = 161) or KA (n = 165). The EuroQol five-dimension questionnaire (EQ-5D) and visual analogue scores (EQ-VAS), Rockwood Clinical Frailty Scale (CFS), and 36-Item Short Form Survey subjective change in HRQoL were assessed at the time and recalled for six months earlier. A state that was WTD was defined as an EQ-5D of less than zero.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