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We aimed to determine the concentrations of synovial vancomycin and meropenem in patients treated by single-stage revision combined with intra-articular infusion following periprosthetic joint infection (PJI), thereby validating this drug delivery approach.

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This study aimed to assess the risk of acute kidney injury (AKI) associated with combined intravenous (IV) and topical antibiotic therapy in patients undergoing treatment for periprosthetic joint infections (PJIs) following total knee arthroplasty (TKA), utilizing the Kidney Disease: Improving Global Outcomes (KDIGO) criteria for classification.

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To explore key stakeholder views around feasibility and acceptability of trials seeking to prevent post-traumatic osteoarthritis (PTOA) following knee injury, and provide guidance for next steps in PTOA trial design.

Aims. Serum inflammatory parameters are widely used to aid in diagnosing a periprosthetic joint infection (PJI). Due to their limited performances in the literature, novel and more accurate biomarkers are needed. Serum albumin-to-globulin ratio (AGR) and serum CRP-to-albumin ratio (CAR) have previously been proposed as potential new parameters, but results were mixed. The aim of this study was to assess the diagnostic accuracy of AGR and CAR in diagnosing PJI and to compare them to the established and widely used marker CRP. Methods. From 2015 to 2022, a consecutive series of 275 cases of revision total hip (n = 129) and knee arthroplasty (n = 146) were included in this retrospective cohort study. Based on the 2021 European Bone and Joint Infection Society (EBJIS) definition, 144 arthroplasties were classified as septic. Using receiver operating characteristic curve (ROC) analysis, the ideal thresholds and diagnostic performances were calculated. The areas under the curve (AUCs) were compared using the z-test. Results. AGR, CAR, and CRP were associated with PJI (p < 0.001). Sensitivities were 62.5% (95% CI 54.3 to 70.0), 73.6% (95% CI 65.8 to 80.1), and 71.5% (95% CI 63.6 to 78.3), respectively. Specificities were calculated with 84.7% (95% CI 77.5 to 89.9), 86.3% (95% CI 79.2 to 91.2), and 87.8% (95% CI 80.9 to 92.4), respectively. The AUC of CRP (0.797 (95% CI 0.750 to 0.843)) was significantly higher than the AUC of AGR (0.736 (95% CI 0.686 to 0.786), p < 0.001), and similar to AUC of CAR (0.799 (95% CI 0.753 to 0.846), p = 0.832). Decreased sensitivities were observed in PJIs caused by low-virulence organisms (AGR: 60%, CAR: 78%) compared to high-virulence pathogens (AGR: 80%, p = 0.042; CAR: 88%, p = 0.158). Higher sensitivities were seen in acute haematogenous (AGR: 83%, CAR: 96%) compared to chronic PJIs (AGR: 54%, p = 0.001; CAR: 65%, p < 0.001). Conclusion. Serum AGR and CAR showed limited diagnostic accuracy (especially in low-grade and chronic infections) and did not outperform the established marker CRP in our study. Hence, neither parameter can be recommended as an additional tool for diagnosing PJI. Cite this article: Bone Joint Res 2024;13(8):372–382

Aims. This study aimed to evaluate the BioFire Joint Infection (JI) Panel in cases of hip and knee periprosthetic joint infection (PJI) where conventional microbiology is unclear, and to assess its role as a complementary intraoperative diagnostic tool. Methods. Five groups representing common microbiological scenarios in hip and knee revision arthroplasty were selected from our arthroplasty registry, prospectively maintained PJI databases, and biobank: 1) unexpected-negative cultures (UNCs), 2) unexpected-positive cultures (UPCs), 3) single-positive intraoperative cultures (SPCs), and 4) clearly septic and 5) aseptic cases. In total, 268 archived synovial fluid samples from 195 patients who underwent acute/chronic revision total hip or knee arthroplasty were included. Cases were classified according to the International Consensus Meeting 2018 criteria. JI panel evaluation of synovial fluid was performed, and the results were compared with cultures. Results. The JI panel detected microorganisms in 7/48 (14.5%) and 15/67 (22.4%) cases related to UNCs and SPCs, respectively, but not in cases of UPCs. The correlation between JI panel detection and infection classification criteria for early/late acute and chronic PJI was 46.6%, 73%, and 40%, respectively. Overall, the JI panel identified 12.6% additional microorganisms and three new species. The JI panel pathogen identification showed a sensitivity and specificity of 41.4% (95% confidence interval (CI) 33.7 to 49.5) and 91.1% (95% CI 84.7 to 94.9), respectively. In total, 19/195 (9.7%) could have been managed differently and more accurately upon JI panel evaluation. Conclusion. Despite its microbial limitation, JI panel demonstrated clinical usefulness by complementing the traditional methods based on multiple cultures, particularly in PJI with unclear microbiological results. Cite this article: Bone Joint Res 2024;13(7):353–361

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The antidiabetic agent metformin inhibits fibrosis in various organs. This study aims to elucidate the effects of hyperglycaemia and metformin on knee joint capsule fibrosis in mice.

Aims. Metal particles detached from metal-on-metal hip prostheses (MoM-THA) have been shown to cause inflammation and destruction of tissues. To further explore this, we investigated the histopathology (aseptic lymphocyte-dominated vasculitis-associated lesions (ALVAL) score) and metal concentrations of the periprosthetic tissues obtained from patients who underwent revision knee arthroplasty. We also aimed to investigate whether accumulated metal debris was associated with ALVAL-type reactions in the synovium. Methods. Periprosthetic metal concentrations in the synovia and histopathological samples were analyzed from 230 patients from our institution from October 2016 to December 2019. An ordinal regression model was calculated to investigate the effect of the accumulated metals on the histopathological reaction of the synovia. Results. Median metal concentrations were as follows: cobalt: 0.69 μg/g (interquartile range (IQR) 0.10 to 6.10); chromium: 1.1 μg/g (IQR 0.27 to 4.10); and titanium: 1.6 μg/g (IQR 0.90 to 4.07). Moderate ALVAL scores were found in 30% (n = 39) of the revised knees. There were ten patients with an ALVAL score of 6 or more who were revised for suspected periprosthetic joint infection (PJI), aseptic loosening, or osteolysis. R2 varied between 0.269 and 0.369 for the ordinal regression models. The most important variables were model type, indication for revision, and cobalt and chromium in the ordinal regression models. Conclusion. We found that metal particles released from the knee prosthesis can accumulate in the periprosthetic tissues. Several patients revised for suspected culture-negative PJI had features of an ALVAL reaction, which is a novel finding. Therefore, ALVAL-type reactions can also be found around knee prostheses, but they are mostly mild and less common than those found around metal-on-metal prostheses. Cite this article: Bone Joint Res 2024;13(4):149–156

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Biofilm infections are among the most challenging complications in orthopaedics, as bacteria within the biofilms are protected from the host immune system and many antibiotics. Halicin exhibits broad-spectrum activity against many planktonic bacteria, and previous studies have demonstrated that halicin is also effective against Staphylococcus aureus biofilms grown on polystyrene or polypropylene substrates. However, the effectiveness of many antibiotics can be substantially altered depending on which orthopaedically relevant substrates the biofilms grow. This study, therefore, evaluated the activity of halicin against less mature and more mature S. aureus biofilms grown on titanium alloy, cobalt-chrome, ultra-high molecular weight polyethylene (UHMWPE), devitalized muscle, or devitalized bone.

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Knee osteoarthritis (OA) involves a variety of tissues in the joint. Gene expression profiles in different tissues are of great importance in order to understand OA.

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Orthopaedic surgery uses many varied instruments with high-speed, high-impact, thermal energy and sometimes heavy instruments, all of which potentially result in aerosolization of contaminated blood, tissue, and bone, raising concerns for clinicians’ health. This study quantifies the aerosol exposure by measuring the number and size distribution of the particles reaching the lead surgeon during key orthopaedic operations.

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The management of periprosthetic joint infection (PJI) remains a major challenge in orthopaedic surgery. In this study, we aimed to characterize the local bone microstructure and metabolism in a clinical cohort of patients with chronic PJI.

Aims. To map the Oxford Knee Score (OKS) and High Activity Arthroplasty Score (HAAS) items to a common scale, and to investigate the psychometric properties of this new scale for the measurement of knee health. Methods. Patient-reported outcome measure (PROM) data measuring knee health were obtained from the NHS PROMs dataset and Total or Partial Knee Arthroplasty Trial (TOPKAT). Assumptions for common scale modelling were tested. A graded response model (fitted to OKS item responses in the NHS PROMs dataset) was used as an anchor to calibrate paired HAAS items from the TOPKAT dataset. Information curves for the combined OKS-HAAS model were plotted. Bland-Altman analysis was used to compare common scale scores derived from OKS and HAAS items. A conversion table was developed to map between HAAS, OKS, and the common scale. Results. We included 3,329 response sets from 528 patients undergoing knee arthroplasty. These generally met the assumptions of unidimensionality, monotonicity, local independence, and measurement invariance. The HAAS items provided more information than OKS items at high levels of knee health. Combining both instruments resulted in higher test-level information than either instrument alone. The mean error between common scale scores derived from the OKS and HAAS was 0.29 logits. Conclusion. The common scale allowed more precise measurement of knee health than use of either the OKS or HAAS individually. These techniques for mapping PROM instruments may be useful for the standardization of outcome reporting, and pooling results across studies that use either PROM in individual-patient meta-analysis. Cite this article: Bone Joint Res 2023;12(10):624–635

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The aim of this study was to investigate the global and local impact of fat on bone in obesity by using the diet-induced obese (DIO) mouse model.

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), 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). Results. Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases. Conclusion. MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases. Cite this article: Bone Joint Res 2023;12(9):512–521

Aims. Focal knee arthroplasty is an attractive alternative to knee arthroplasty for young patients because it allows preservation of a large amount of bone for potential revisions. However, the mechanical behaviour of cartilage has not yet been investigated because it is challenging to evaluate in vivo contact areas, pressure, and deformations from metal implants. Therefore, this study aimed to determine the contact pressure in the tibiofemoral joint with a focal knee arthroplasty using a finite element model. Methods. The mechanical behaviour of the cartilage surrounding a metal implant was evaluated using finite element analysis. We modelled focal knee arthroplasty with placement flush, 0.5 mm deep, or protruding 0.5 mm with regard to the level of the surrounding cartilage. We compared contact stress and pressure for bone, implant, and cartilage under static loading conditions. Results. Contact stress on medial and lateral femoral and tibial cartilages increased and decreased, respectively, the most and the least in the protruding model compared to the intact model. The deep model exhibited the closest tibiofemoral contact stress to the intact model. In addition, the deep model demonstrated load sharing between the bone and the implant, while the protruding and flush model showed stress shielding. The data revealed that resurfacing with a focal knee arthroplasty does not cause increased contact pressure with deep implantation. However, protruding implantation leads to increased contact pressure, decreased bone stress, and biomechanical disadvantage in an in vivo application. Conclusion. These results show that it is preferable to leave an edge slightly deep rather than flush and protruding. Cite this article: Bone Joint Res 2023;12(8):497–503

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Cite this article: Bone Joint Res 2023;12(7):447–454.

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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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The aim of this investigation was to compare risk of infection in both cemented and uncemented hemiarthroplasty (HA) as well as in total hip arthroplasty (THA) following femoral neck fracture.

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Arthroplasty surgery of the knee and hip is performed in two to three million patients annually. Periprosthetic joint infections occur in 4% of these patients. Debridement, antibiotics, and implant retention (DAIR) surgery aimed at cleaning the infected prosthesis often fails, subsequently requiring invasive revision of the complete prosthetic reconstruction. Infection-specific imaging may help to guide DAIR. In this study, we evaluated a bacteria-specific hybrid tracer (^99mTc-UBI_29-41-Cy5) and its ability to visualize the bacterial load on femoral implants using clinical-grade image guidance methods.

Cite this article: Bone Joint Res 2022;11(12):890–892.