Background: A large body of evidence is emerging to implicate that dysregulation of the gut microbiome (dysbiosis) increases the risk of surgical site infections. Gut dysbiosis is known to occur in patients with inflammatory bowel disease (IBD), allowing for translocation of bacteria across the inflamed and highly permeable intestinal mucosal wall. The null hypothesis was that IBD was not associated with an increased risk of periprosthetic joint infection (PJI) after primary total hip and knee arthroplasty.Methods: A matched cohort study was designed. The primary end point was the occurrence of PJI at 2 years postoperatively. The secondary end points were aseptic revisions at 2 years postoperatively, discharge to a rehabilitation facility, complications up to 30 days after total joint arthroplasty, and readmission up to 90 days after total joint arthroplasty. The International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes were used to identify patients with IBD and the control cohort. A chart review was performed to confirm the diagnosis of IBD. Using our institutional database, 152 patients with IBD were identified and matched (3:1) for age, sex, body mass index, year of surgical procedure, Charlson Comorbidity Index, and involved joint with 456 patients without IBD undergoing total joint arthroplasty. Results:The cumulative incidence of PJI was 4.61% for the patients with IBD compared with 0.88% for the control cohort (p = 0.0024). When univariable Cox regression was performed, a diagnosis of IBD was found to be an independent risk factor for PJI (hazard ratio [HR], 5.44 [95% confidence interval (CI), 1.59 to 18.60]; p = 0.007) and aseptic revisions (HR, 4.02 [95% CI, 1.50 to 10.79]; p = 0.006). The rate of postoperative complications was also higher in patients with IBD.Conclusions: Based on the findings of this study, it appears that patients with IBD are at higher risk for treatment failure due to PJI or aseptic loosening after primary total joint arthroplasty. The exact reason for this finding is not known, but could be related to bacterial translocation from the inflamed intestinal mucosa, the dysregulated inflammatory status of these patients, malnutrition, and potentially other factors. Some of the aseptic failures could be as a result of infection that may have escaped detection and/or recognition.

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Background: Although periprosthetic joint infection (PJI) can affect multiple joints concurrently, the majority of patients with multiple prosthetic joints present with PJI of a single joint. Data regarding the optimal management of these patients are limited. We aimed to identify the prevalence, risk factors for a subsequent PJI, and clinical circumstances of PJI in patients with multiple prosthetic joints. Methods: We retrospectively reviewed the clinical records of 197 patients with ≥2 total joint prostheses in place who presented with PJI from 2000 to 2017. The average follow-up was 3.6 years (range, 0.5 to 17 years). Demographic data and risk factors for synchronous or metachronous PJI were identified. The time from the initial to the second PJI and organism profile data were collected as well. The workup for other joints with a prosthesis in place at the time of the initial PJI was noted. Results: Among the 197 patients with PJI and multiple joint prostheses in situ, 37 (19%) developed PJI in another joint; 11 had a synchronous PJI and 26 had a metachronous PJI. The average time between the first and the second infection in the metachronous cases was 848 days (range, 20 to 3,656 days). Females and patients with an initial PJI with methicillin-resistant Staphylococcus aureus (MRSA) were more likely to have a metachronous PJI, and patients with rheumatoid arthritis had an increased risk of a second (metachronous or synchronous) PJI. Three of 11 patients in the synchronous group and 19% (5) of the 26 in the metachronous group had bacteremia at the time of the initial PJI compared with 12% (19) of the 160 with a single PJI. The percentage of negative cultures increased from 10% for the initial PJIs to 38% for the metachronous PJIs. Conclusions: Patients who have multiple prosthetic joints in place and present with PJI of a single joint are at risk of developing PJI in another joint. Female sex, rheumatoid arthritis, bacteremia at presentation, and infection with MRSA appear to be risk factors for PJI of another joint. Clinical evaluation of the other prosthetic joint(s) should be carried out in all patients and aspiration of those joint(s) should be considered for patients with any of the above risk factors. Level of Evidence: Prognostic Level IV. See Instructions for Authors for a complete description of levels of evidence.

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Venous thromboembolism (VTE) and major bleeding (MBE) are feared complications that are influenced by numerous host and surgical related factors. Using machine learning on contemporary data, our aim was to develop and validate a practical, easy-to-use algorithm to predict risk for VTE and MBE following total joint arthroplasty (TJA). This was a single institutional study of 35,963 primary and revision total hip (THA) and knee arthroplasty (TKA) patients operated between 2009 and 2020. Fifty-six variables related to demographics, comorbidities, operative factors as well as chemoprophylaxis were included in the analysis. The cohort was divided to training (70%) and test (30%) sets. Four machine learning models were developed for each of the outcomes assessed (VTE and MBE). Models were created for all VTE grouped together as well as for pulmonary emboli (PE) and deep vein thrombosis (DVT) individually to examine the need for distinct algorithms. For each outcome, the model that best performed using repeated cross validation was chosen for algorithm development, and predicted versus observed incidences were evaluated. Of the 35,963 patients included, 308 (0.86%) developed VTE (170 PE’s, 176 DVT’s) and 293 (0.81%) developed MBE. Separate models were created for PE and DVT as they were found to outperform the prediction of VTE. Gradient boosting trees had the highest performance for both PE (AUC-ROC 0.774 [SD 0.055]) and DVT (AUC-ROC 0.759 [SD 0.039]). For MBE, least absolute shrinkage and selection operator (Lasso) analysis had the highest AUC (AUC-ROC 0.803 [SD 0.035]). An algorithm that provides the probability for PE, DVT and MBE for each specific patient was created. All 3 algorithms had good discriminatory capability and cross-validation showed similar probabilities comparing predicted and observed failures indicating high accuracy of the model. We successfully developed and validated an easy-to-use algorithm that accurately predicts VTE and MBE following TJA. This tool can be used in every-day clinical decision making and patient counseling.