Septic arthritis of the hip often leads to irreversible osteoarthritis (OA) and the requirement for total hip arthroplasty (THA). The aim of this study was to report the mid-term risk of any infection, periprosthetic joint infection (PJI), aseptic revision, and reoperation in patients with a past history of septic arthritis who underwent THA, compared with a control group of patients who underwent THA for OA. We retrospectively identified 256 THAs in 244 patients following septic arthritis of the native hip, which were undertaken between 1969 and 2016 at a single institution. Each case was matched 1:1, based on age, sex, BMI, and year of surgery, to a primary THA performed for OA. The mean age and BMI were 58 years (35 to 84) and 31 kg/m2 (18 to 48), respectively, and 100 (39%) were female. The mean follow-up was 11 years (2 to 39).Aims
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Aims.
Periprosthetic joint infection (PJI) in total hip arthroplasty in the elderly may occur but has been subject to limited investigation. This study analyzed infection characteristics, surgical outcomes, and perioperative complications of octogenarians undergoing treatment for PJI in a single university-based institution. We identified 33 patients who underwent treatment for PJIs of the hip between January 2010 and December 2019 using our institutional joint registry. Mean age was 82 years (80 to 90), with 19 females (57%) and a mean BMI of 26 kg/m2 (17 to 41). Mean American Society of Anesthesiologists (ASA) grade was 3 (1 to 4) and mean Charlson Comorbidity Index was 6 (4 to 10). Leading pathogens included coagulase-negative Staphylococci (45%) and Aims
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This study compares the re-revision rate and mortality following septic and aseptic revision hip arthroplasty (rTHA) in registry data, and compares the outcomes to previously reported data. This is an observational cohort study using data from the German Arthroplasty Registry (EPRD). A total of 17,842 rTHAs were included, and the rates and cumulative incidence of hip re-revision and mortality following septic and aseptic rTHA were analyzed with seven-year follow-up. The Kaplan-Meier estimates were used to determine the re-revision rate and cumulative probability of mortality following rTHA.Aims
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This study aims to assess the relationship between history of pseudotumour formation secondary to metal-on-metal (MoM) implants and periprosthetic joint infection (PJI) rate, as well as establish ESR and CRP thresholds that are suggestive of infection in these patients. We hypothesized that patients with a pseudotumour were at increased risk of infection. A total of 1,171 total hip arthroplasty (THA) patients with MoM articulations from August 2000 to March 2014 were retrospectively identified. Of those, 328 patients underwent metal artefact reduction sequence MRI and had minimum two years’ clinical follow-up, and met our inclusion criteria. Data collected included demographic details, surgical indication, laterality, implants used, history of pseudotumour, and their corresponding preoperative ESR (mm/hr) and CRP (mg/dl) levels. Multivariate logistic regression modelling was used to evaluate PJI and history of pseudotumour, and receiver operating characteristic curves were created to assess the diagnostic capabilities of ESR and CRP to determine the presence of infection in patients undergoing revision surgery.Aims
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One-stage revision hip arthroplasty for periprosthetic joint infection (PJI) has several advantages; however, resection of the proximal femur might be necessary to achieve higher success rates. We investigated the risk factors for resection and re-revisions, and assessed complications and subsequent re-revisions. In this single-centre, case-control study, 57 patients who underwent one-stage revision arthroplasty for PJI of the hip and required resection of the proximal femur between 2009 and 2018 were identified. The control group consisted of 57 patients undergoing one-stage revision without bony resection. Logistic regression analysis was performed to identify any correlation with resection and the risk factors for re-revisions. Rates of all-causes re-revision, reinfection, and instability were compared between groups.Aims
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Although there is increasing legalization of the use of cannabis in the USA, few well-powered studies have evaluated the association between cannabis use disorder and outcomes following primary total hip arthroplasty (THA). Thus, the aim of this study was to determine whether patients who use cannabis and undergo primary THA have higher rates of in-hospital length of stay (LOS), medical complications, implant-related complications, and costs. Using an administrative database, patients with cannabis use disorder undergoing primary THA were matched to a control group in a 1:5 ratio by age, sex, and various medical comorbidities. This yielded 23,030 patients (3,842 in the study group matched with 19,188 in the control group). The variables which were studied included LOS, 90-day medical complications, two-year implant-related complications, and 90-day costs of care. Mann-Whitney U tests were used to compare LOS and costs. Multivariate logistic regression analyses were used to calculate the odds ratios (ORs) of developing complications.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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