The aims of this study were to determine the diagnostic yield of image-guided biopsy in providing a final diagnosis in patients with suspected infectious spondylodiscitis, to report the diagnostic accuracy of various microbiological tests and histological examinations in these patients, and to report the epidemiology of infectious spondylodiscitis from a country where tuberculosis (TB) is endemic, including the incidence of drug-resistant TB. A total of 284 patients with clinically and radiologically suspected infectious spondylodiscitis were prospectively recruited into the study. Image-guided biopsy of the vertebral lesion was performed and specimens were sent for various microbiological tests and histological examinations. The final diagnosis was determined using a composite reference standard based on clinical, radiological, serological, microbiological, and histological findings. The overall diagnostic yield of the biopsy, and that for each test, was calculated in light of the final diagnosis.Aims
Methods
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. In this prospective study, 177 specimens were collected from 59 revision arthroplasties, including periprosthetic tissues, synovial fluid, and prosthetic sonicate fluid. Each specimen was divided into two, one for mNGS and one for culture. The criteria of the Musculoskeletal Infection Society were used to define PJI (40 cases) and aseptic failure (19 cases).Aims
Methods
Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.
Fungal and mycobacterial periprosthetic joint infections (PJI) are rare events. Clinicians are wary of missing these diagnoses, often leading to the routine ordering of fungal and mycobacterial cultures on periprosthetic specimens. Our goal was to examine the utility of these cultures and explore a modern bacterial culture technique using bacterial blood culture bottles (BCBs) as an alternative. We performed a retrospective review of patients diagnosed with hip or knee PJI between 1 January 2010 and 31 December 2019, at the Mayo Clinic in Rochester, Minnesota, USA. We included patients aged 18 years or older who had fungal, mycobacterial, or both cultures performed together with bacterial cultures. Cases with positive fungal or mycobacterial cultures were reviewed using the electronic medical record to classify the microbiological findings as representing true infection or not.Aims
Methods
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
Methods
This study aimed to answer two questions: what are the best diagnostic methods for diagnosing bacterial arthritis of a native joint?; and what are the most commonly used definitions for bacterial arthritis of a native joint? We performed a search of PubMed, Embase, and Cochrane libraries for relevant studies published between January 1980 and April 2020. Of 3,209 identified studies, we included 27 after full screening. Sensitivity, specificity, area under the curve, and Youden index of diagnostic tests were extracted from included studies. We grouped test characteristics per diagnostic modality. We extracted the definitions used to establish a definitive diagnosis of bacterial arthritis of a native joint per study.Aims
Methods
We aimed to describe the epidemiological, biological, and bacteriological characteristics of osteoarticular infections (OAIs) caused by The medical charts of all children presenting with OAIs to our institution over a 13-year period (January 2007 to December 2019) were reviewed. Among these patients, we extracted those which presented an OAI caused by Aims
Methods
This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional machine-learning algorithm. Variables collected from 28,742 patients were analyzed based on their contribution to hospital length of stay.Aims
Methods
The diagnosis of developmental dysplasia of the hip (DDH) is challenging owing to extensive variation in paediatric pelvic anatomy. Artificial intelligence (AI) may represent an effective diagnostic tool for DDH. Here, we aimed to develop an anteroposterior pelvic radiograph deep learning system for diagnosing DDH in children and analyze the feasibility of its application. In total, 10,219 anteroposterior pelvic radiographs were retrospectively collected from April 2014 to December 2018. Clinicians labelled each radiograph using a uniform standard method. Radiographs were grouped according to age and into ‘dislocation’ (dislocation and subluxation) and ‘non-dislocation’ (normal cases and those with dysplasia of the acetabulum) groups based on clinical diagnosis. The deep learning system was trained and optimized using 9,081 radiographs; 1,138 test radiographs were then used to compare the diagnoses made by deep learning system and clinicians. The accuracy of the deep learning system was determined using a receiver operating characteristic curve, and the consistency of acetabular index measurements was evaluated using Bland-Altman plots.Aims
Methods
The diagnosis of periprosthetic joint infection (PJI) has always been challenging. Recently, D-dimer has become a promising biomarker in diagnosing PJI. However, there is controversy regarding its diagnostic value. We aim to investigate the diagnostic value of D-dimer in comparison to ESR and CRP. PubMed, Embase, and the Cochrane Library were searched in February 2020 to identify articles reporting on the diagnostic value of D-dimer on PJI. Pooled analysis was conducted to investigate the diagnostic value of D-dimer, CRP, and ESR.Aims
Methods
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
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
Methods
The aim of this study was to evaluate the performance of metagenomic next-generation sequencing (mNGS) in detecting pathogens from synovial fluid of prosthetic joint infection (PJI) patients. A group of 75 patients who underwent revision knee or hip arthroplasties were enrolled prospectively. Ten patients with primary arthroplasties were included as negative controls. Synovial fluid was collected for mNGS analysis. Optimal thresholds were determined to distinguish pathogens from background microbes. Synovial fluid, tissue, and sonicate fluid were obtained for culture.Aims
Methods
This pilot study tested the performance of a rapid assay for diagnosing prosthetic joint infection (PJI), which measures synovial fluid calprotectin from total hip and knee revision patients. A convenience series of 69 synovial fluid samples from revision patients at the Norfolk and Norwich University Hospital were collected intraoperatively (52 hips, 17 knees) and frozen. Synovial fluid calprotectin was measured retrospectively using a new commercially available lateral flow assay for PJI diagnosis (Lyfstone AS) and compared to International Consensus Meeting (ICM) 2018 criteria and clinical case review (ICM-CR) gold standards.Aims
Methods
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
Methods
To assess the diagnostic value of C-reactive protein (CRP), leucocyte count (LC), and erythrocyte sedimentation rate (ESR) in late fracture-related infection (FRI). PubMed, Embase, and Cochrane databases were searched focusing on the diagnostic value of CRP, LC, and ESR in late FRI. Sensitivity and specificity combinations were extracted for each marker. Average estimates were obtained using bivariate mixed effects models.Aims
Materials and Methods
Abnormal femoral torsion (FT) is increasingly recognized as an additional cause for femoroacetabular impingement (FAI). It is unknown if in-toeing of the foot is a specific diagnostic sign for increased FT in patients with symptomatic FAI. The aims of this study were to determine: 1) the prevalence and diagnostic accuracy of in-toeing to detect increased FT; 2) if foot progression angle (FPA) and tibial torsion (TT) are different among patients with abnormal FT; and 3) if FPA correlates with FT. A retrospective, institutional review board (IRB)-approved, controlled study of 85 symptomatic patients (148 hips) with FAI or hip dysplasia was performed in the gait laboratory. All patients had a measurement of FT (pelvic CT scan), TT (CT scan), and FPA (optical motion capture system). We allocated all patients to three groups with decreased FT (< 10°, 37 hips), increased FT (> 25°, 61 hips), and normal FT (10° to 25°, 50 hips). Cluster analysis was performed.Aims
Patients and Methods
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
Methods
