Synovial fluid white blood cell (WBC) count and percentage of polymorphonuclear cells (%PMN) are elevated at periprosthetic joint infection (PJI). Leucocytes produce different interleukins (IL), including IL-6, so we hypothesized that synovial fluid IL-6 could be a more accurate predictor of PJI than synovial fluid WBC count and %PMN. The main aim of our study was to compare the predictive performance of all three diagnostic tests in the detection of PJI. Patients undergoing total hip or knee revision surgery were included. In the perioperative assessment phase, synovial fluid WBC count, %PMN, and IL-6 concentration were measured. Patients were labeled as positive or negative according to the predefined cut-off values for IL-6 and WBC count with %PMN. Intraoperative samples for microbiological and histopathological analysis were obtained. PJI was defined as the presence of sinus tract, inflammation in histopathological samples, and growth of the same microorganism in a minimum of two or more samples out of at least four taken.Aims
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To identify the prevalence of neuropathic pain after lower limb fracture surgery, assess associations with pain severity, quality of life and disability, and determine baseline predictors of chronic neuropathic pain at three and at six months post-injury. Secondary analysis of a UK multicentre randomized controlled trial (Wound Healing in Surgery for Trauma; WHiST) dataset including adults aged 16 years or over following surgery for lower limb major trauma. The trial recruited 1,547 participants from 24 trauma centres. Neuropathic pain was measured at three and six months using the Doleur Neuropathique Questionnaire (DN4); 701 participants provided a DN4 score at three months and 781 at six months. Overall, 933 participants provided DN4 for at least one time point. Physical disability (Disability Rating Index (DRI) 0 to 100) and health-related quality-of-life (EuroQol five-dimension five-level; EQ-5D-5L) were measured. Candidate predictors of neuropathic pain included sex, age, BMI, injury mechanism, concurrent injury, diabetes, smoking, alcohol, analgaesia use pre-injury, index surgery location, fixation type, Injury Severity Score, open injury, and wound care.Aims
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Accurate estimations of the risk of fracture due to metastatic bone disease in the femur is essential in order to avoid both under-treatment and over-treatment of patients with an impending pathological fracture. The purpose of the current retrospective in vivo study was to use CT-based finite element analyses (CTFEA) to identify a clear quantitative differentiating factor between patients who are at imminent risk of fracturing their femur and those who are not, and to identify the exact location of maximal weakness where the fracture is most likely to occur. Data were collected on 82 patients with femoral metastatic bone disease, 41 of whom did not undergo prophylactic fixation. A total of 15 had a pathological fracture within six months following the CT scan, and 26 were fracture-free during the five months following the scan. The Mirels score and strain fold ratio (SFR) based on CTFEA was computed for all patients. A SFR value of 1.48 was used as the threshold for a pathological fracture. The sensitivity, specificity, positive, and negative predicted values for Mirels score and SFR predictions were computed for nine patients who fractured and 24 who did not, as well as a comparison of areas under the receiver operating characteristic curves (AUC of the ROC curves).Aims
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To develop and externally validate a parsimonious statistical prediction model of 90-day mortality after elective total hip arthroplasty (THA), and to provide a web calculator for clinical usage. We included 53,099 patients with cemented THA due to osteoarthritis from the Swedish Hip Arthroplasty Registry for model derivation and internal validation, as well as 125,428 patients from England and Wales recorded in the National Joint Register for England, Wales, Northern Ireland, the Isle of Man, and the States of Guernsey (NJR) for external model validation. A model was developed using a bootstrap ranking procedure with a least absolute shrinkage and selection operator (LASSO) logistic regression model combined with piecewise linear regression. Discriminative ability was evaluated by the area under the receiver operating characteristic curve (AUC). Calibration belt plots were used to assess model calibration.Aims
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This study aimed to evaluate calprotectin in synovial fluid for diagnosing chronic prosthetic joint infection (PJI) . A total of 63 patients who were suspected of PJI were enrolled. The synovial fluid calprotectin was tested by an enzyme-linked immunosorbent assay (ELISA). Laboratory test data, such as ESR, CRP, synovial fluid white blood cells (SF-WBCs), and synovial fluid polymorphonuclear cells (SF-PMNs), were documented. Chi-squared tests were used to compare the sensitivity and specificity of calprotectin and laboratory tests. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was calculated to determine diagnostic efficacy.Aims
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To evaluate if union of clavicle fractures can be predicted at six weeks post-injury by the presence of bridging callus on ultrasound. Adult patients managed nonoperatively with a displaced mid-shaft clavicle were recruited prospectively. Ultrasound evaluation of the fracture was undertaken to determine if sonographic bridging callus was present. Clinical risk factors at six weeks were used to stratify patients at high risk of nonunion with a combination of Quick Disabilities of the Arm, Shoulder and Hand questionnaire (QuickDASH) ā„ 40, fracture movement on examination, or absence of callus on radiograph.Aims
Methods
Microbiological culture is a key element in the diagnosis of periprosthetic joint infection (PJI). However, cultures of periprosthetic tissue do not have optimal sensitivity. One of the main reasons for this is that microorganisms are not released from the tissues, either due to biofilm formation or intracellular persistence. This study aimed to optimize tissue pretreatment methods in order to improve detection of microorganisms. From December 2017 to September 2019, patients undergoing revision arthroplasty in a single centre due to PJI and aseptic failure (AF) were included, with demographic data and laboratory test results recorded prospectively. Periprosthetic tissue samples were collected intraoperatively and assigned to tissue-mechanical homogenization (T-MH), tissue-manual milling (T-MM), tissue-dithiothreitol (T-DTT) treatment, tissue-sonication (T-S), and tissue-direct culture (T-D). The yield of the microbial cultures was then analyzed.Aims
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The aim of this study was to evaluate the ability of a machine-learning algorithm to diagnose prosthetic loosening from preoperative radiographs and to investigate the inputs that might improve its performance. A group of 697 patients underwent a first-time revision of a total hip (THA) or total knee arthroplasty (TKA) at our institution between 2012 and 2018. Preoperative anteroposterior (AP) and lateral radiographs, and historical and comorbidity information were collected from their electronic records. Each patient was defined as having loose or fixed components based on the operation notes. We trained a series of convolutional neural network (CNN) models to predict a diagnosis of loosening at the time of surgery from the preoperative radiographs. We then added historical data about the patients to the best performing model to create a final model and tested it on an independent dataset.Aims
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We aimed to report the mid- to long-term rates of septic and aseptic failure after two-stage revision surgery for periprosthetic joint infection (PJI) following total hip arthroplasty (THA). We retrospectively reviewed 96 cases which met the Musculoskeletal Infection Society criteria for PJI. The mean follow-up was 90 months (SD 32). Septic failure was assessed using a Delphi-based consensus definition. Any further surgery undertaken for aseptic mechanical causes was considered as aseptic failure. The cumulative incidence with competing risk analysis was used to predict the risk of septic failure. A regression model was used to evaluate factors associated with septic failure. The cumulative incidence of aseptic failure was also analyzed.Aims
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We quantitatively compared the 3D bone density distributions on CT scans performed on scaphoid waist fractures subacutely that went on to union or nonunion, and assessed whether 2D CT evaluations correlate with 3D bone density evaluations. We constructed 3D models from 17 scaphoid waist fracture CTs performed between four to 18 weeks after fracture that did not unite (nonunion group), 17 age-matched scaphoid waist fracture CTs that healed (union group), and 17 age-matched control CTs without injury (control group). We measured the 3D bone density for the distal and proximal fragments relative to the triquetrum bone density and compared findings among the three groups. We then performed bone density measurements using 2D CT and evaluated the correlation with 3D bone densities. We identified the optimal cutoff with diagnostic values of the 2D method to predict nonunion with receiver operating characteristic (ROC) curves.Aims
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The aim of this study was to measure the effect of hospital case volume on the survival of revision total knee arthroplasty (RTKA). This is a retrospective analysis of Scottish Arthroplasty Project data, a nationwide audit which prospectively collects data on all arthroplasty procedures performed in Scotland. The primary outcome was RTKA survival at ten years. The primary explanatory variable was the effect of hospital case volume per year on RTKA survival. Kaplan-Meier survival curves were plotted with 95% confidence intervals (CIs) to determine the lifespan of RTKA. Multivariate Cox proportional hazards were used to estimate relative revision risks over time. Hazard ratios (HRs) were reported with 95% CI, and p-value < 0.05 was considered statistically significant.Aims
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Prosthetic joint infection (PJI) diagnosis is a major challenge in orthopaedics, and no reliable parameters have been established for accurate, preoperative predictions in the differential diagnosis of aseptic loosening or PJI. This study surveyed factors in synovial fluid (SF) for improving PJI diagnosis. We enrolled 48 patients (including 39 PJI and nine aseptic loosening cases) who required knee/hip revision surgery between January 2016 and December 2017. The PJI diagnosis was established according to the Musculoskeletal Infection Society (MSIS) criteria. SF was used to survey factors by protein array and enzyme-linked immunosorbent assay to compare protein expression patterns in SF among three groups (aseptic loosening and first- and second-stage surgery). We compared routine clinical test data, such as C-reactive protein level and leucocyte number, with potential biomarker data to assess the diagnostic ability for PJI within the same patient groups.Objectives
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The lack of disease-modifying treatments for osteoarthritis (OA) is linked to a shortage of suitable biomarkers. This study combines multi-molecule synovial fluid analysis with machine learning to produce an accurate diagnostic biomarker model for end-stage knee OA (esOA). Synovial fluid (SF) from patients with esOA, non-OA knee injury, and inflammatory knee arthritis were analyzed for 35 potential markers using immunoassays. Partial least square discriminant analysis (PLS-DA) was used to derive a biomarker model for cohort classification. The ability of the biomarker model to diagnose esOA was validated by identical wide-spectrum SF analysis of a test cohort of ten patients with esOA.Aims
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