Aims. To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total
Aims. For the increasing number of working-age patients undergoing total
Aims. Bone stock restoration of acetabular bone defects using impaction bone grafting (IBG) in total hip arthroplasty may facilitate future re-revision in the event of failure of the reconstruction. We hypothesized that the acetabular bone defect during re-revision surgery after IBG was smaller than during the previous revision surgery. The clinical and radiological results of re-revisions with repeated use of IBG were also analyzed. Methods. In a series of 382 acetabular revisions using IBG and a cemented component, 45
Aims. The aim of this meta-analysis was to determine the pooled incidence of postoperative urinary retention (POUR) following total
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. Methods. 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),
Aims. 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. Methods. 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. Results. Of the 455 studies identified, only 12 were suitable for inclusion. Nine reported implant identification and three described predicting risk of implant failure. Of the 12, three studies compared AI performance with orthopaedic surgeons. AI-based implant identification achieved AUC 0.992 to 1, and most algorithms reported an accuracy > 90%, using 550 to 320,000 training radiographs. AI prediction of dislocation risk post-THA, determined after five-year follow-up, was satisfactory (AUC 76.67; 8,500 training radiographs). Diagnosis of
Aims. The first aim of this study was to evaluate whether preoperative renal function is associated with postoperative changes in whole blood levels of metal ions in patients who have undergone a Birmingham
Aims. The aim of this study was to investigate the association between the type of operation used to treat a trochanteric fracture of the
Aims. Day-case success rates after primary total hip arthroplasty (THA), total knee arthroplasty (TKA), and medial unicompartmental knee arthroplasty (mUKA) may vary, and detailed data are needed on causes of not being discharged. The aim of this study was to analyze the association between surgical procedure type and successful day-case surgery, and to analyze causes of not being discharged on the day of surgery when eligible and scheduled for day-case THA, TKA, and mUKA. Methods. A multicentre, prospective consecutive cohort study was carried out from September 2022 to August 2023. Patients were screened for day-case eligibility using well defined inclusion and exclusion criteria, and discharged when fulfilling predetermined discharge criteria. Day-case eligible patients were scheduled for surgery with intended start of surgery before 1.00 pm. Results. Of 6,142 primary
Aims. Delirium is associated with adverse outcomes following
Aims. This study aims to describe the pre- and postoperative self-reported health and quality of life from a national cohort of patients undergoing elective total conventional hip arthroplasty (THA) and total knee arthroplasty (TKA) in Australia. For context, these data will be compared with patient-reported outcome measures (PROMs) data from other international nation-wide registries. Methods. Between 2018 to 2020, and nested within a nationwide arthroplasty registry, preoperative and six-month postoperative PROMs were electronically collected from patients before and after elective THA and TKA. There were 5,228 THA and 8,299 TKA preoperative procedures as well as 3,215 THA and 4,982 TKA postoperative procedures available for analysis. Validated PROMs included the EuroQol five-dimension five-level questionnaire (EQ-5D-5L; range 0 to 100; scored worst-best health), Oxford
Aims.
Aims. Our main aim was to describe the trend in the comorbidities of patients undergoing elective total hip arthroplasties (THAs) and knee arthroplasties (KAs) between 1 January 2005 and 31 December 2018 in England. Methods. We combined data from the National Joint Registry (NJR) on primary elective
Aims. Successful cell therapy in
Aims. The aim of this study was to determine the prevalence of depressive and anxiety disorders prior to total
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
Aims. Breast cancer survivors have known risk factors that might influence the results of total hip arthroplasty (THA) or total knee arthroplasty (TKA). This study evaluated clinical outcomes of patients with breast cancer history after primary THA and TKA. Methods. Our total joint registry identified patients with breast cancer history undergoing primary THA (n = 423) and TKA (n = 540). Patients were matched 1:1 based upon age, sex, BMI, procedure (hip or knee), and surgical year to non-breast cancer controls. Mortality, implant survival, and complications were assessed via Kaplan-Meier methods. Clinical outcomes were evaluated via Harris
Aims. Despite the COVID-19 pandemic, incidence of
Aims. The aim of this study was to assess the association of mortality and reoperation when comparing cemented and uncemented hemiarthroplasty (HA) in