Aims. The influence of metabolic syndrome (MetS) on the outcome after
Aims. This study aimed to investigate the estimated change in primary and revision arthroplasty rate in the Netherlands and Denmark for
Aims. Our aim was to estimate the total costs of all hospitalizations for treating periprosthetic joint infection (PJI) by main management strategy within 24 months post-diagnosis using activity-based costing. Additionally, we investigated the influence of individual PJI treatment pathways on hospital costs within the first 24 months. Methods. Using admission and procedure data from a prospective observational cohort in Australia and New Zealand, Australian Refined Diagnosis Related Groups were assigned to each admitted patient episode of care for activity-based costing estimates of 273
Aims. To review the evidence and reach consensus on recommendations for follow-up after total
Aims. Histology is widely used for diagnosis of persistent infection during reimplantation in two-stage revision
Aims. The aim of this study was to report health-related quality of life (HRQoL) and joint-specific function in patients waiting for total
Aims. We investigated the efficacy and safety profile of commonly used venous thromboembolism (VTE) prophylaxis agents following
Aims. The extended wait that most patients are now experiencing for
Aims. The purpose of this study was to determine the association between prior sleeve gastrectomy in patients undergoing primary total
Aims. The primary aim was to assess change in health-related quality of life (HRQoL) of patients as they waited from six to 12 months for a total
Aims. 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
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. The aim of this meta-analysis was to determine the pooled incidence of postoperative urinary retention (POUR) following total
Aims. Nonagenarians (aged 90 to 99 years) have experienced the fastest percent decile population growth in the USA recently, with a consequent increase in the prevalence of nonagenarians living with joint arthroplasties. As such, the number of revision total hip arthroplasties (THAs) and total knee arthroplasties (TKAs) in nonagenarians is expected to increase. We aimed to determine the mortality rate, implant survivorship, and complications of nonagenarians undergoing aseptic revision THAs and revision TKAs. Methods. Our institutional total joint registry was used to identify 96 nonagenarians who underwent 97 aseptic revisions (78
Aims. The aim of this study was to conduct a cross-sectional, observational cohort study of patients presenting for revision of a 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. 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. 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