Aims. National joint registries under-report revisions for periprosthetic joint infection (PJI). We aimed to validate PJI reporting to the Australian Orthopaedic Association National Joint Arthroplasty Registry (AOANJRR) and the factors associated with its accuracy. We then applied these data to refine estimates of the total national burden of PJI. Methods. A total of 561 Australian cases of confirmed PJI were captured by a large, prospective observational study, and matched to data available for the same patients through the AOANJRR. Results. In all, 501 (89.3%) cases of PJI recruited to the prospective observational study were successfully matched with the AOANJRR database. Of these, 376 (75.0%) were captured by the registry, while 125 (25.0%) did not have a revision or reoperation for PJI recorded. In a multivariate logistic regression analysis, early (within 30 days of implantation) PJIs were less likely to be reported (adjusted odds ratio (OR) 0.56; 95% confidence interval (CI) 0.34 to 0.93; p = 0.020), while two-stage revision procedures were more likely to be reported as a PJI to the registry (OR 5.3 (95% CI 2.37 to 14.0); p ≤ 0.001) than debridement and implant retention or other surgical procedures. Based on this data, the true estimate of the incidence of PJI in Australia is up to 3,900 cases per year. Conclusion. In Australia, infection was not recorded as the indication for revision or reoperation in one-quarter of those with confirmed PJI. This is better than in other registries, but suggests that registry-captured estimates of the total national burden of PJI are underestimated by at least one-third. Inconsistent PJI reporting is multifactorial but could be improved by developing a nested PJI registry embedded within the national arthroplasty registry. Cite this article: Bone Jt Open 2022;3(5):367–373

Objectives. Computed tomography (CT) plays an important role in evaluating wear and periacetabular osteolysis (PAO) in total hip replacements. One concern with CT is the high radiation exposure since standard pelvic CT provides approximately 3.5 millisieverts (mSv) of radiation exposure, whereas a planar radiographic examination with three projections totals approximately 0.5 mSv. The objective of this study was to evaluate the lowest acceptable radiation dose for dual-energy CT (DECT) images when measuring wear and periacetabular osteolysis in uncemented metal components. Materials and Methods. A porcine pelvis with bilateral uncemented hip prostheses and with known linear wear and acetabular bone defects was examined in a third-generation multidetector DECT scanner. The examinations were performed with four different radiation levels both with and without iterative reconstruction techniques. From the high and low peak kilo voltage acquisitions, polychrmoatic images were created together with virtual monochromatic images of energies 100 kiloelectron volts (keV) and 150 keV. Results. We could assess wear and PAO while substantially lowering the effective radiation dose to 0.7 mSv for a total pelvic view with an accuracy of around 0.5 mm for linear wear and 2 mm to 3 mm for PAO. Conclusion. CT for detection of prosthetic wear and PAO could be used with clinically acceptable accuracy at a radiation exposure level equal to plain radiographic exposures. Cite this article: B. Sandgren, M. Skorpil, P. Nowik, H. Olivecrona, J. Crafoord, L. Weidenhielm, A. Persson. Assessment of wear and periacetabular osteolysis using dual energy computed tomography on a pig cadaver to identify the lowest acceptable radiation dose. Bone Joint Res 2016;5:307–313. DOI: 10.1302/2046-3758.57.2000566

Aims. The aim of this study was to determine the diagnostic accuracy of α defensin (AD) lateral flow assay (LFA) and enzyme-linked immunosorbent assay (ELISA) tests for periprosthetic joint infection (PJI) in comparison to conventional synovial white blood cell (WBC) count and polymorphonuclear neutrophil percentage (PMN%) analysis. Methods. Patients undergoing joint aspiration for evaluation of pain after total knee arthroplasty (TKA) or total hip arthroplasty (THA) were considered for inclusion. Synovial fluids from 99 patients (25 THA and 74 TKA) were analyzed by WBC count and PMN% analysis, AD LFA, and AD ELISA. WBC and PMN% cutoffs of ≥ 1,700 cells/mm. 3. and ≥ 65% for TKA and ≥ 3,000 cells/mm. 3. and ≥ 80% for THA were used, respectively. A panel of three physicians, all with expertise in orthopaedic infections and who were blinded to the results of AD tests, independently reviewed patient data to diagnose subjects as with or without PJI. Consensus PJI classification was used as the reference standard to evaluate test performances. Results were compared using McNemar’s test and area under the receiver operating characteristic curve (AUC) analysis. Results. Expert consensus classified 18 arthroplasies as having failed due to PJI and 81 due to aseptic failure. Using these classifications, the calculated sensitivity and specificity of AD LFA was 83.3% (95% confidence interval (CI) 58.6 to 96.4) and 93.8% (95% CI 86.2 to 98.0), respectively. Sensitivity and specificity of AD ELISA was 83.3% (95% CI 58.6 to 96.4) and 96.3% (95% CI 89.6 to 99.2), respectively. There was no statistically significant difference between sensitivity (p = 1.000) or specificity (p = 0.157) of the two AD assays. AUC for AD LFA was 0.891. In comparison, AUC for synovial WBC count, PMN%, and the combination of the two values was 0.821 (sensitivity p = 1.000, specificity p < 0.001), 0.886 (sensitivity p = 0.317, specificity p = 0.011), and 0.926 (sensitivity p = 0.317, specificity p = 0.317), respectively. Conclusion. The diagnostic accuracy of synovial AD for PJI diagnosis is comparable and not statistically superior to that of synovial WBC count plus PMN% combined. Cite this article: Bone Joint J 2021;103-B(6):1119–1126

Using general practitioner records and hospital notes and through direct telephone conversation with patients, we investigated the accuracy of nine patient-reported complications gathered from a self-completed questionnaire after elective joint replacement surgery of the hip and knee. A total of 402 post-discharge complications were reported after 8546 elective operations that were undertaken within a three-year period. These were reported by 136 men and 240 women with a mean age of 71.8 years (34 to 93). A total of 319 reported complications (79.4%; 95% confidence interval 75.4 to 83.3) were confirmed to be correct. High rates of correct reporting were demonstrated for infection (94.5%) and the need for further surgery (100%), whereas the rates of reporting deep-vein thrombosis (DVT), pulmonary embolism, myocardial infarction and stroke were lower (75% to 84.2%). Dislocation, peri-prosthetic fractures and nerve palsy had modest rates of correct reporting (36% to 57.1%). More patients who had knee surgery delivered incorrect reports of dislocation (p = 0.001) and DVT (p = 0.013). Despite these variations, it appears that post-operative complications may form part of a larger patient-reported outcome programme after elective joint replacement surgery

Aims. The aim of this study was to evaluate the diagnostic accuracy of the synovial alpha-defensin enzyme-linked immunosorbent assay (ELISA) for the diagnosis of prosthetic joint infection (PJI) in the work-up prior to revision of total hip (THA) and knee arthroplasty (TKA). Patients and Methods. Inclusion criteria for this prospective cohort study were acute or chronic symptoms of the index joint without specific exclusion criteria. Synovial fluid aspirates of 202 patients were analyzed and semiquantitative laboratory alpha-defensin ELISA was performed. Final diagnosis of PJI was established by examination of samples obtained during revision surgery. Results. Sensitivity and specificity of the alpha-defensin ELISA for PJI were 78.2% (95% confidence interval (CI) 66.7 to 88.5) and 96.6% (95% CI 93.0 to 99.3). Positive and negative predictive values were 89.6% (95% CI 80.6 to 97.8) and 92.2% (95% CI 87.5 to 96.1). The test remained false-negative in 22% of septic revisions, most of which were due to coagulase-negative staphylococci all occurring in either late-chronic or early-postoperative PJI. Conclusion. The routine use of synovial fluid alpha-defensin laboratory ELISA in the preoperative evaluation of symptomatic THAs and TKAs is insufficient to accurately diagnose PJI. Particularly in cases involving low-virulence organisms, such as coagulase-negative staphylococci, there remains a need for tests with a higher sensitivity. Cite this article: Bone Joint J 2019;101-B:970–977

Aims. The aims of this study were to increase the diagnostic accuracy of the analysis of synovial fluid in the differentiation of prosthetic joint infection (PJI) by the addition of inexpensive biomarkers such as the levels of C-reactive protein (CRP), adenosine deaminase (ADA), alpha-2-macrogloblulin (α2M) and procalcitonin. Patients and Methods. Between January 2013 and December 2015, synovial fluid and removed implants were requested from 143 revision total joint arthroplasties. A total of 55 patients met inclusion criteria of the receipt of sufficient synovial fluid, tissue samples and removed implants for analysis. The diagnosis of PJI followed the definition from a recent International Consensus Meeting to create two groups of patients; septic and aseptic. Using receiver operating characteristic curves we determined the cutoff values and diagnostic accuracy for each marker. Results. There were 23 PJIs and 32 patients with aseptic loosening. The levels of total leucocyte count, proportion of polymorphonuclear leucocytes (PMNs), CRP, ADA and α2M in the synovial fluid were all significantly higher in those with a PJI than in those with aseptic loosening. The levels of procalcitonin were comparable in the two groups. Cutoff values for the optimal performance in the diagnosis of infection were: total leucocyte count > 1463 cells/μL (sensitivity (Sens) 100%, specificity (Spec) 71.9%, positive predictive value (PPV) 71.9%, negative predictive value (NPV) 100%); proportion of PMNs > 81% (Sens 78.3%, Spec 75.0%, PPV 69.2%, NPV 82.8%); CRP > 6.7mg/L (Sens 78.3%, Spec 93.8%, PPV 90.0%, NPV 85.7%); ADA > 61U/L (Sens 78.3%, Spec 96.9%, PPV 94.7%, NPV 86.1%) and α2M > 958 mg/L (Sens 47.8%, Spec 96.9%, PPV 91.7%, NPV 72.1%). The addition of a raised level of CRP or ADA to the total leukocyte count increased the specificity: total leukocyte count > 1463 cells/μL and CRP > 6.7mg/L (Sens 78.3%, Spec 100%, PPV 100%, NPV 86.5%) or with ADA > 61U/L (Sens 78.3%, Spec 96.9%, PPV 94.7%, NPV 86.1%). . Conclusion. The total leucocyte count in the synovial fluid offers great negative predictive value in the diagnosis of PJI and the addition of more specific markers such as CRP and ADA improves the positive predictive value. Thus the addition of simple and inexpensive markers to the measurement of the leucocyte count in the synovial fluid may reduce the number of equivocal results which demand more expensive investigation. Cite this article: Bone Joint J 2017;99-B:351–7

Aims. 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. Methods. We posed the 20 most Google-searched questions about THA and TKA, plus ten additional postoperative questions, to ChatGPT. Each question was asked twice to evaluate for consistency in quality. Following each response, we responded with, “Please explain so it is easier to understand,” to evaluate ChatGPT’s ability to reduce response reading grade level, measured as Flesch-Kincaid Grade Level (FKGL). Five resident physicians rated the 120 responses on 1 to 5 accuracy and comprehensiveness scales. Additionally, they answered a “yes” or “no” question regarding acceptability. Mean scores were calculated for each question, and responses were deemed acceptable if ≥ four raters answered “yes.”. Results. The mean accuracy and comprehensiveness scores were 4.26 (95% confidence interval (CI) 4.19 to 4.33) and 3.79 (95% CI 3.69 to 3.89), respectively. Out of all the responses, 59.2% (71/120; 95% CI 50.0% to 67.7%) were acceptable. ChatGPT was consistent when asked the same question twice, giving no significant difference in accuracy (t = 0.821; p = 0.415), comprehensiveness (t = 1.387; p = 0.171), acceptability (χ. 2. = 1.832; p = 0.176), and FKGL (t = 0.264; p = 0.793). There was a significantly lower FKGL (t = 2.204; p = 0.029) for easier responses (11.14; 95% CI 10.57 to 11.71) than original responses (12.15; 95% CI 11.45 to 12.85). Conclusion. ChatGPT answered THA and TKA patient questions with accuracy comparable to previous reports of websites, with adequate comprehensiveness, but with limited acceptability as the sole information source. ChatGPT has potential for answering patient questions about THA and TKA, but needs improvement. Cite this article: Bone Jt Open 2024;5(2):139–146

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. 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. The aim of this study was to evaluate the diagnostic accuracy of the absolute synovial polymorphonuclear neutrophil cell (PMN) count for the diagnosis or exclusion of periprosthetic joint infection (PJI) after total hip (THA) or knee arthroplasty (TKA). Methods. In this retrospective cohort study, 147 consecutive patients with acute or chronic complaints following THA and TKA were included. Diagnosis of PJI was established based on the 2018 International Consensus Meeting criteria. A total of 39 patients diagnosed with PJI (32 chronic and seven acute) and 108 patients with aseptic complications were surgically revised. Results. Using receiver operating characteristic curves and calculating the area under the curve (AUC), an optimal synovial cut-off value of 2,000 PMN/µl was determined (AUC 0.978 (95% confidence interval (CI) 0.946 to 1)). Using this cut-off, sensitivity and specificity of absolute synovial PMN count for PJI were 97.4% (95% CI 91.2 to 100) and 93.5% (95% CI 88.9 to 98.1), respectively. Positive and negative predictive value were 84.4% (95% CI 72.7 to 93.9) and 99.0% (95% CI 96.7 to 100), respectively. Exclusion of 20 patients with acute complications improved specificity to 97.9% (95% CI 94.6 to 100). Different cut-off values for THA (< 3,600 PMN/µl) and TKA (< 2,000 PMN/µl) were identified. Absolute synovial PMN count correlated strongly with synovial alpha-defensin (AD) (r = 0.759; p < 0.001). With a positive AD result, no additional PJI could be identified in any case. Conclusion. Absolute synovial PMN count is a widely available, rapid, cost-effective, and accurate marker in PJI diagnostics, whereas synovial AD appears to be a surrogate parameter of absolute synovial PMN count. Despite limitations in the early postoperative phase, wear, and rheumatic diseases in confirming PJI, an absolute synovial PMN count below 2,000/µl is highly suitable for ruling out PJI, with specific cut-off values for THA and TKA. Cite this article: Bone Joint J 2023;105-B(4):373–381

Aims. 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. Methods. We retrospectively collected data from 226 patients (90 hips, 136 knees) with periprosthetic joint infection who underwent two-stage revision between August 2011 and September 2021, with a minimum follow-up of one year. Histology was assessed via the SLIM classification. First, we analyzed whether patients with positive permanent sections at reimplantation had higher reinfection rates than patients with negative histology. Further, we compared permanent and frozen section results, and assessed the influence of anatomical regions (knee versus hip), low- versus high-grade infections, as well as first revision versus multiple prior revisions on the histological result at reimplantation. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), chi-squared tests, and Kaplan-Meier estimates were calculated. Results. Overall, the reinfection rate was 18%. A total of 14 out of 82 patients (17%) with positive permanent sections at reimplantation experienced reinfection, compared to 26 of 144 patients (18%) with negative results (p = 0.996). Neither permanent sections nor fresh frozen sections were significantly associated with reinfection, with a sensitivity of 0.35, specificity of 0.63, PPV of 0.17, NPV of 0.81, and accuracy of 58%. Histology was not significantly associated with reinfection or survival time for any of the analyzed sub-groups. Permanent and frozen section results were in agreement for 91% of cases. Conclusion. Permanent and fresh frozen sections at reimplantation in two-stage revision do not serve as a reliable predictor for reinfection. Cite this article: Bone Joint J 2024;106-B(4):372–379

Aims. 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. Methods. 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. Results. In total, 49 joints in 48 patients (mean age 68 years (SD 10; 26 females (54%), 25 knees (51%)) were included. Of these 11 joints (22%) were infected. The synovial fluid WBC count and %PMN predicted PJI with sensitivity, specificity, accuracy, PPV, and NPV of 82%, 97%, 94%, 90%, and 95%, respectively. Synovial fluid IL-6 predicted PJI with sensitivity, specificity, accuracy, PPV, and NPV of 73%, 95%, 90%, 80%, and 92%, respectively. A comparison of predictive performance indicated a strong agreement between tests. Conclusions. Synovial fluid IL-6 is not superior to synovial fluid WBC count and %PMN in detecting PJI. Level of Evidence: Therapeutic Level II. Cite this article: Bone Jt Open 2020;1-12:737–742

Aims. The aim of this study was to further evaluate the accuracy of ten promising synovial biomarkers (bactericidal/permeability-increasing protein (BPI), lactoferrin (LTF), neutrophil gelatinase-associated lipocalin (NGAL), neutrophil elastase 2 (ELA-2), α-defensin, cathelicidin LL-37 (LL-37), human β-defensin (HBD-2), human β-defensin 3 (HBD-3), D-dimer, and procalcitonin (PCT)) for the diagnosis of periprosthetic joint infection (PJI), and to investigate whether inflammatory joint disease (IJD) activity affects their concentration in synovial fluid. Methods. We included 50 synovial fluid samples from patients with (n = 25) and without (n = 25) confirmed PJI from an institutional tissue bank collected between May 2015 and December 2016. We also included 22 synovial fluid samples aspirated from patients with active IJD presenting to Department of Rheumatology, the first Medical Centre, Chinese PLA General Hospital. Concentrations of the ten candidate biomarkers were measured in the synovial fluid samples using standard enzyme-linked immunosorbent assays (ELISA). The diagnostic accuracy was evaluated by receiver operating characteristic (ROC) curves. Results. BPI, LTF, NGAL, ELA-2, and α-defensin were well-performing biomarkers for detecting PJI, with areas under the curve (AUCs) of 1.000 (95% confidence interval, 1.000 to 1.000), 1.000 (1.000 to 1.000), 1.000 (1.000 to 1.000), 1.000 (1.000 to 1.000), and 0.998 (0.994 to 1.000), respectively. The other markers (LL-37, HBD-2, D-dimer, PCT, and HBD-3) had limited diagnostic value. For the five well-performing biomarkers, elevated concentrations were observed in patients with active IJD. The original best thresholds determined by the Youden index, which discriminated PJI cases from non-PJI cases could not discriminate PJI cases from active IJD cases, while elevated thresholds resulted in good performance. Conclusion. BPI, LTF, NGAL, ELA-2, and α-defensin demonstrated excellent performance for diagnosing PJI. However, all five markers showed elevated concentrations in patients with IJD activity. For patients with IJD, elevated thresholds should be considered to accurately diagnose PJI. Cite this article: Bone Joint J 2021;103-B(1):32–38

Aims. The diagnosis of periprosthetic joint infection (PJI) can be difficult. All current diagnostic tests have problems with accuracy and interpretation of results. Many new tests have been proposed, but there is no consensus on the place of many of these in the diagnostic pathway. Previous attempts to develop a definition of PJI have not been universally accepted and there remains no reference standard definition. Methods. This paper reports the outcome of a project developed by the European Bone and Joint Infection Society (EBJIS), and supported by the Musculoskeletal Infection Society (MSIS) and the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) Study Group for Implant-Associated Infections (ESGIAI). It comprised a comprehensive review of the literature, open discussion with Society members and conference delegates, and an expert panel assessment of the results to produce the final guidance. Results. This process evolved a three-level approach to the diagnostic continuum, resulting in a definition set and guidance, which has been fully endorsed by EBJIS, MSIS, and ESGIAI. Conclusion. The definition presents a novel three-level approach to diagnosis, based on the most robust evidence, which will be useful to clinicians in daily practice. Cite this article: Bone Joint J 2021;103-B(1):18–25

Aims. Metagenomic next-generation sequencing (mNGS) is useful in the diagnosis of infectious disease. However, while it is highly sensitive at identifying bacteria, it does not provide information on the sensitivity of the organisms to antibiotics. The purpose of this study was to determine whether the results of mNGS can be used to guide optimization of culture methods to improve the sensitivity of culture from intraoperative samples. Methods. Between July 2014 and October 2019, patients with suspected joint infection (JI) from whom synovial fluid (SF) was obtained preoperatively were enrolled. Preoperative aspirated SF was analyzed by conventional microbial culture and mNGS. In addition to samples taken for conventional microbial culture, some samples were taken for intraoperative culture to optimize the culture method according to the preoperative mNGS results. The demographic characteristics, medical history, laboratory examination, mNGS, and culture results of the patients were recorded, and the possibility of the optimized culture methods improving diagnostic efficiency was evaluated. Results. A total of 56 cases were included in this study. There were 35 cases of JI and 21 cases of non-joint infection (NJI). The sensitivity, specificity, and accuracy of intraoperative microbial culture after optimization of the culture method were 94.29%, 76.19%, and 87.5%, respectively, while those of the conventional microbial culture method were 60%, 80.95%, and 67.86%, respectively. Conclusion. Preoperative aspirated SF detected via mNGS can provide more aetiological information than preoperative culture, which can guide the optimization and improve the sensitivity of intraoperative culture. Cite this article: Bone Joint J 2021;103-B(1):39–45

Aims. Use of molecular sequencing methods in periprosthetic joint infection (PJI) diagnosis and organism identification have gained popularity. Next-generation sequencing (NGS) is a potentially powerful tool that is now commercially available. The purpose of this study was to compare the diagnostic accuracy of NGS, polymerase chain reaction (PCR), conventional culture, the Musculoskeletal Infection Society (MSIS) criteria, and the recently proposed criteria by Parvizi et al in the diagnosis of PJI. Methods. In this retrospective study, aspirates or tissue samples were collected in 30 revision and 86 primary arthroplasties for routine diagnostic investigation for PJI and sent to the laboratory for NGS and PCR. Concordance along with statistical differences between diagnostic studies were calculated. Results. Using the MSIS criteria to diagnose PJI as the reference standard, the sensitivity and specificity of NGS were 60.9% and 89.9%, respectively, while culture resulted in sensitivity of 76.9% and specificity of 95.3%. PCR had a low sensitivity of 18.4%. There was no significant difference based on sample collection method (tissue swab or synovial fluid) (p = 0.760). There were 11 samples that were culture-positive and NGS-negative, of which eight met MSIS criteria for diagnosing infection. Conclusion. In our series, NGS did not provide superior sensitivity or specificity results compared to culture. PCR has little utility as a standalone test for PJI diagnosis with a sensitivity of only 18.4%. Currently, several laboratory tests for PJI diagnosis should be obtained along with the overall clinical picture to help guide decision-making for PJI treatment. Cite this article: Bone Joint J 2021;103-B(1):26–31

Aims. Biopsy of the periprosthetic tissue is an important diagnostic tool for prosthetic joint infection (PJI) as it enables the detection of the responsible microorganism with its sensitivity to antibiotics. We aimed to investigate how often the bacteria identified in the tissue analysis differed between samples obtained from preoperative biopsy and intraoperative revision surgery in cases of late PJI; and whether there was a therapeutic consequence. Methods. A total of 508 patients who required revision surgery of total hip arthroplasty (THA) (n = 231) or total knee arthroplasty (TKA) (n = 277) because of component loosening underwent biopsy before revision surgery. The tissue samples collected at biopsy and during revision surgery were analyzed according to the criteria of the Musculoskeletal Infection Society (MSIS). Results. In total, 178 (113 THA, 65 TKA) were classified as infected. The biopsy procedure had a sensitivity of 93.8%, a specificity of 97.3%, a positive predictive value (PPV) of 94.9%, a negative predictive value (NPV) of 96.7%, and an accuracy of 96.1%. Of the 178 infected patients, 26 showed a difference in the detected bacteria from the biopsy and the revision surgery (14.6%). This difference required a change to antibiotic therapy in only two cases (1.1%). Conclusion. Biopsy is a useful tool to diagnose PJI, but there may be a difference in the detected bacteria between the biopsy and revision surgery. However, this did not affect the choice of antibiotic therapy in most cases, rendering the clinical relevance of this phenomenon as low. Cite this article: Bone Joint J 2020;102-B(3):329–335

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The aim of this study was to evaluate the optimal deep tissue specimen sample number for histopathological analysis in the diagnosis of periprosthetic joint infection (PJI).

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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.

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.