Aims. Fungal periprosthetic joint infections (fPJIs) are rare complications, constituting only 1% of all PJIs. Neither a uniform definition for fPJI has been established, nor a standardized treatment regimen. Compared to bacterial PJI, there is little evidence for fPJI in the literature with divergent results. Hence, we implemented a novel treatment algorithm based on three-stage revision arthroplasty, with local and systemic antifungal therapy to optimize treatment for fPJI. Methods. From 2015 to 2018, a total of 18 patients with fPJI were included in a prospective, single-centre study (DKRS-ID 00020409). The diagnosis of PJI is based on the European Bone and Joint Infection Society definition of periprosthetic joint infections. The baseline parameters (age, sex, and BMI) and additional data (previous surgeries, pathogen spectrum, and Charlson Comorbidity Index) were recorded. A therapy protocol with three-stage revision, including a scheduled spacer exchange, was implemented. Systemic antifungal medication was administered throughout the entire treatment period and continued for six months after reimplantation. A minimum follow-up of 24 months was defined. Results. Eradication of infection was achieved in 16 out of 18 patients (88.8%), with a mean follow-up of 35 months (25 to 54). Mixed bacterial and fungal infections were present in seven cases (39%). The interval period, defined as the period of time from explantation to reimplantation, was 119 days (55 to 202). In five patients, a salvage procedure was performed (three cementless modular knee arthrodesis, and two Girdlestone procedures). Conclusion. Therapy for fPJI is complex, with low cure rates according to the literature. No uniform treatment recommendations presently exist for fPJI. Three-stage revision arthroplasty with prolonged systemic antifungal therapy showed promising results. Cite this article: Bone Jt Open 2021;2(8):671–678

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

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. This study aimed to develop a virtual clinic for the purpose of reducing face-to-face orthopaedic consultations. Patients and Methods. Anonymized experts (hip and knee arthroplasty patients, surgeons, physiotherapists, radiologists, and arthroplasty practitioners) gave feedback via a Delphi Consensus Technique. This consisted of an iterative sequence of online surveys, during which virtual documents, made up of a patient-reported questionnaire, standardized radiology report, and decision-guiding algorithm, were modified until consensus was achieved. We tested the patient-reported questionnaire on seven patients in orthopaedic clinics using a ‘think-aloud’ process to capture difficulties with its completion. Results. A patient-reported 13-item questionnaire was developed covering pain, mobility, and activity. The radiology report included up to ten items (e.g. progressive periprosthetic bone loss) depending on the type of arthroplasty. The algorithm concludes in one of three outcomes: review at surgeon’s discretion (three to 12 months); see at next available clinic; or long-term follow-up/discharge. Conclusion. The virtual clinic approach with attendant documents achieved consensus by orthopaedic experts, radiologists, and patients. The robust development and testing of this standardized virtual clinic provided a sound platform for organizations in the United Kingdom to adopt a virtual clinic approach for follow-up of hip and knee arthroplasty patients. Cite this article: Bone Joint J 2019;101-B:951–959

Aims. To develop and validate patient-centred algorithms that estimate individual risk of death over the first year after elective joint arthroplasty surgery for osteoarthritis. Methods. A total of 763,213 hip and knee joint arthroplasty episodes recorded in the National Joint Registry for England and Wales (NJR) and 105,407 episodes from the Norwegian Arthroplasty Register were used to model individual mortality risk over the first year after surgery using flexible parametric survival regression. Results. The one-year mortality rates in the NJR were 10.8 and 8.9 per 1,000 patient-years after hip and knee arthroplasty, respectively. The Norwegian mortality rates were 9.1 and 6.0 per 1,000 patient-years, respectively. The strongest predictors of death in the final models were age, sex, body mass index, and American Society of Anesthesiologists grade. Exposure variables related to the intervention, with the exception of knee arthroplasty type, did not add discrimination over patient factors alone. Discrimination was good in both cohorts, with c-indices above 0.76 for the hip and above 0.70 for the knee. Time-dependent Brier scores indicated appropriate estimation of the mortality rate (≤ 0.01, all models). Conclusion. Simple demographic and clinical information may be used to calculate an individualized estimation for one-year mortality risk after hip or knee arthroplasty (. https://jointcalc.shef.ac.uk. ). These models may be used to provide patients with an estimate of the risk of mortality after joint arthroplasty. Cite this article: Bone Joint Res 2020;9(11):808–820

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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.

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The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA).

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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 aim of this study was to determine the prevalence of depressive and anxiety disorders prior to total hip (THA) and total knee arthroplasty (TKA) and to assess their impact on the rates of any infection, revision, or reoperation.

Aims. The aim of this study was to identify the incidence of positive cultures during the second stage of a two-stage revision arthroplasty and to analyse the association between positive cultures and an infection-free outcome. Patients and Methods. This single-centre retrospective review of prospectively collected data included patients with a periprosthetic joint infection (PJI) of either the hip or the knee between 2013 and 2015, who were treated using a standardised diagnostic and therapeutic algorithm with two-stage exchange. Failure of treatment was assessed according to a definition determined by a Delphi-based consensus. Logistic regression analysis was performed to assess the predictors of positive culture and risk factors for failure. The mean follow-up was 33 months (24 to 48). Results. A total of 163 two-stage revision arthroplasties involving 84 total hip arthroplasties (THAs) and 79 total knee arthroplasties (TKAs) were reviewed. In 27 patients (16.6%), ≥ 1 positive culture was identified at re-implantation and eight (29.6%) of these subsequently failed compared with 20 (14.7%) patients who were culture-negative. The same initially infecting organism was isolated at re-implantation in nine of 27 patients (33.3%). The organism causing re-infection in none of the patients was the same as that isolated at re-implantation. The risk of the failure of treatment was significantly higher in patients with a positive culture (odds ratio (OR) 1.7; 95% confidence interval (CI) 1.0 to 3.0; p = 0.049) and in patients with a higher Charlson Comorbidity Index (OR 1.5; 95% CI 1.6 to 1.8; p = 0.001). Conclusion. Positive culture at re-implantation was independently associated with subsequent failure. Surgeons need to be aware of this association and should consider the medical optimisation of patients with severe comorbidities both before and during treatment. Cite this article: Bone Joint J 2017;99-B:1490–5

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We aim to evaluate the usefulness of postoperative blood tests by investigating the incidence of abnormal results following total joint replacement (TJR), as well as identifying preoperative risk factors for abnormal blood test results postoperatively, especially pertaining to anaemia and acute kidney injury (AKI).

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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.

During the last ten years, greater attention has been given to the management of peri-operative blood loss after total knee arthroplasty (TKA), as it is a modifiable outcome that has a significant effect on the rate of complications, the recovery, and the economic burden. Blood loss after TKA has been greatly reduced during this time, thereby dramatically reducing the rates of allogeneic transfusion. This has significantly reduced the complications associated with transfusion, such as fluid overload, infection, and increased length of stay. . The greatest advent in lowering peri-operative blood loss after TKA has been the introduction of tranexamic acid, which reduces blood loss without increasing the risk of thromboembolic events. . This paper discusses the ways of reducing blood loss after TKA, for which a multimodal algorithm, with pre-, intra- and post-operative measures, has been adopted at our institution. Cite this article: Bone Joint J 2016;98-B(1 Suppl A):98–100

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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.

Aims . Recently, several synovial biomarkers have been introduced into the algorithm for the diagnosis of a prosthetic joint infection (PJI). Alpha defensin is a promising biomarker, with a high sensitivity and specificity, but it is expensive. Calprotectin is a protein that is present in the cytoplasm of neutrophils, is released upon neutrophil activation and exhibits anti-microbial activity. Our aim, in this study, was to determine the diagnostic potential of synovial calprotectin in the diagnosis of a PJI. Patients and Methods. In this pilot study, we prospectively collected synovial fluid from the hip, knee, shoulder and elbow of 19 patients with a proven PJI and from a control group of 42 patients who underwent revision surgery without a PJI. PJI was diagnosed according to the current diagnostic criteria of the Musculoskeletal Infection Society. Synovial fluid was centrifuged and the supernatant was used to measure the level of calprotectin after applying a lateral flow immunoassay. . Results. The median synovial calprotectin level was 991 mg/L (interquartile range (IQR) 154 to 1787) in those with a PJI and 11 mg/L (IQR 3 to 29) in the control group (p < 0.0001). Using a cut-off value of 50 mg/L, this level showed an excellent diagnostic accuracy, with an area under the curve of 0.94. The overall sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) was 89%, 90%, 81% and 95% respectively. The NPV was 97% in the nine patients with a chronic PJI. . Conclusion . Synovial calprotectin may be a valuable biomarker in the diagnosis of a PJI, especially in the exclusion of an infection. With a lateral flow immunoassay, a relatively rapid quantitative diagnosis can be made. The measurement is cheap and is easy to use. . Cite this article: Bone Joint J 2017;99-B:660–5

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The aim of this study was to conduct a cross-sectional, observational cohort study of patients presenting for revision of a total hip, or total or unicompartmental knee arthroplasty, to understand current routes to revision surgery and explore differences in symptoms, healthcare use, reason for revision, and the revision surgery (surgical time, components, length of stay) between patients having regular follow-up and those without.

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To determine the trajectories of patient reported pain and functional disability over five years following total hip arthroplasty (THA) or total knee arthroplasty (TKA).

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As a proven and comprehensive molecular technique, metagenomic next-generation sequencing (mNGS) has shown its potential in the diagnosis of pathogens in patients with periprosthetic joint infection (PJI), using a single type of specimen. However, the optimal use of mNGS in the management of PJI has not been explored. In this study, we evaluated the diagnostic value of mNGS using three types of specimen with the aim of achieving a better choice of specimen for mNGS in these patients.

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Total joint replacement (TJR) is a high-cost, high-volume procedure that impacts patients’ quality of life. Informed decisions are important for patients facing TJR. The quality of information provided by websites regarding TJR is highly variable. We aimed to measure the quality of TJR information online.

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Currently, the US Center for Medicaid and Medicare Services (CMS) has been testing bundled payments for revision total joint arthroplasty (TJA) through the Bundled Payment for Care Improvement (BPCI) programme. Under the BPCI, bundled payments for revision TJAs are defined on the basis of diagnosis-related groups (DRGs). However, these DRG-based bundled payment models may not be adequate to account appropriately for the varying case-complexity seen in revision TJAs.