Aims. Periprosthetic joint infection (PJI) demonstrates the most feared complication after total joint replacement (TJR). The current work analyzes the demographic, comorbidity, and complication profiles of all patients who had in-hospital treatment due to PJI. Furthermore, it aims to evaluate the in-hospital mortality of patients with PJI and analyze possible risk factors in terms of secondary diagnosis, diagnostic procedures, and complications. Methods. In a retrospective, cross-sectional study design, we gathered all patients with PJI (International Classification of Diseases (ICD)-10 code: T84.5) and resulting in-hospital treatment in Germany between 1 January 2019 and 31 December 2022. Data were provided by the Institute for the Hospital Remuneration System in Germany. Demographic data, in-hospital deaths, need for intensive care therapy, secondary diagnosis, complications, and use of diagnostic instruments were assessed. Odds ratios (ORs) with 95% confidence intervals (CIs) for in-hospital mortality were calculated. Results. A total of 52,286 patients were included, of whom 1,804 (3.5%) died. Hypertension, diabetes mellitus, and obesity, the most frequent comorbidities, were not associated with higher in-hospital mortality. Cardiac diseases as atrial fibrillation, cardiac pacemaker, or three-vessel coronary heart disease showed the highest risk for in-hospital mortality. Postoperative anaemia occurred in two-thirds of patients and showed an increased in-hospital mortality (OR 1.72; p < 0.001). Severe complications, such as organ failure, systemic inflammatory response syndrome (SIRS), or septic shock syndrome showed by far the highest association with in-hospital mortality (OR 39.20; 95% CI 33.07 to 46.46; p < 0.001). Conclusion. These findings highlight the menace coming from PJI. It can culminate in multi-organ failure, SIRS, or septic shock syndrome, along with very high rates of in-hospital mortality, thereby highlighting the vulnerability of these patients. Particular attention should be paid to patients with cardiac comorbidities such as atrial fibrillation or three-vessel coronary heart disease. Risk factors should be optimized preoperatively, anticoagulant therapy stopped and restarted on time, and sufficient patient blood management should be emphasized. Cite this article: Bone Jt Open 2024;5(4):367–373

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 hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Methods. 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. Results. For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts. Conclusion. The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts. Cite this article: Bone Joint J 2024;106-B(7):688–695

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 work packages focusing on opinion from key stakeholders (patients and surgeons). First, an online modified Delphi process to determine a consensus set of factors that should be involved in patient prioritization. This will be performed using standard Delphi methodology consisting of multiple rounds where following initial individual rating there is feedback, discussion, and further recommendations undertaken towards eventual consensus. The second stage will then consist of a Discrete Choice Experiment (DCE) to allow for priority setting of the factors derived from the Delphi through elicitation of weighted benefit scores. The DCE consists of several choice tasks designed to elicit stakeholder preference regarding included attributes (factors). Results. The study is co-funded by the University of Aberdeen Knowledge Exchange Commission (Ref CF10693-29) and a Chief Scientist Office (CSO) Scotland Clinical Research Fellowship which runs from 08/2021 to 08/2024 (Grant ref: CAF/21/06). Approval from the University of Aberdeen Institute of Applied Health Sciences School Ethics Review Board was granted 22/03/2022 - Reference number SERB/2021/12/2210. Conclusion. The PATHWAY project provides the first attempt to use patient and surgeon opinions to develop a unified approach to prioritization for those awaiting hip and knee arthroplasty. Development of such a tool will provide more equitable access to arthroplasty services, as well as providing a framework for developing similar approaches in other areas of healthcare delivery. Cite this article: Bone Jt Open 2022;3(10):753–758

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 hip implant loosening was good (accuracy 88.3%; 420 training radiographs) and measurement of postoperative acetabular angles was comparable to humans (mean absolute difference 1.35° to 1.39°). However, 11 of the 12 studies had several methodological limitations introducing a high risk of bias. None of the studies were externally validated. Conclusion. These studies show that AI is promising. While it already has the ability to analyze images with significant precision, there is currently insufficient high-level evidence to support its widespread clinical use. Further research to design robust studies that follow standard reporting guidelines should be encouraged to develop AI models that could be easily translated into real-world conditions. Cite this article: Bone Joint J 2022;104-B(8):929–937

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The primary aim of this prospective, multicentre study is to describe the rates of returning to golf following hip, knee, ankle, and shoulder arthroplasty in an active golfing population. Secondary aims will include determining the timing of return to golf, changes in ability, handicap, and mobility, and assessing joint-specific and health-related outcomes following surgery.

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The present study aimed to investigate whether patients with inflammatory bowel disease (IBD) undergoing joint arthroplasty have a higher incidence of adverse outcomes than those without IBD.

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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 work on long-term outcomes, and even less regarding TFA undertaken for non-oncological indications. The aim of this study was to evaluate the long-term clinical outcomes of all TFAs in our unit.

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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.

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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).

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For the increasing number of working-age patients undergoing total hip or total knee arthroplasty (THA/TKA), return to work (RTW) after surgery is crucial. We investigated the association between occupational class and time to RTW after THA or TKA.

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The aim of this meta-analysis was to determine the pooled incidence of postoperative urinary retention (POUR) following total hip and knee arthroplasty (total joint replacement (TJR)) and to evaluate the risk factors and complications associated with POUR.

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Revision total knee arthroplasty (rTKA) and revision total hip arthroplasty (rTHA) are complex procedures with higher rates of re-revision, complications, and mortality compared to primary TKA and THA. We report the effects of the establishment of a revision arthroplasty network (the East Midlands Specialist Orthopaedic Network; EMSON) on outcomes of rTKA and rTHA.

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A higher failure rate has been reported in haematogenous periprosthetic joint infection (PJI) compared to non-haematogenous PJI. The reason for this difference is unknown. We investigated the outcome of haematogenous and non-haematogenous PJI to analyze the risk factors for failure in both groups of patients.

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The outcomes of patients with unexpected positive cultures (UPCs) during revision total hip arthroplasty (THA) and total knee arthroplasty (TKA) remain unknown. The objectives of this study were to establish the prevalence and infection-free implant survival in UPCs during presumed aseptic single-stage revision THA and TKA at mid-term follow-up.

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The surgical helmet system (SHS) was developed to reduce the risk of periprosthetic joint infection (PJI), but the evidence is contradictory, with some studies suggesting an increased risk of PJI due to potential leakage through the glove-gown interface (GGI) caused by its positive pressure. We assumed that SHS and glove exchange had an impact on the leakage via GGI.

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Histology is widely used for diagnosis of persistent infection during reimplantation in two-stage revision hip and knee arthroplasty, although data on its utility remain scarce. Therefore, this study aims to assess the predictive value of permanent sections at reimplantation in relation to reinfection risk, and to compare results of permanent and frozen sections.

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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).

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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.

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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.

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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.