In the United States, racial disparities have been observed in complications following total joint arthroplasty (TJA), including readmissions and mortality. It is unclear whether such disparities also exist for periprosthetic joint infection (PJI). The clinical data registry of a large New England hospital system was used to identify patients who underwent TJA between January 2018 and December 2021. The comorbidities were evaluated using the Elixhauser Comorbidity Index (ECI). We used Poisson regression to assess the relationship between PJI and race by estimating cumulative incidence ratios (cIRs) and 95% confidence intervals (CIs). We adjusted for age and sex and examined whether ECI was a mediator using structural equation modeling. The final analytic dataset included 10,018 TJAs in 9681 individuals [mean age (SD) 69 (10)]. The majority (96.5%) of the TJAs were performed in non-Hispanic (NH) White individuals. The incidence of PJI was higher among NH Black individuals (3.1%) compared with NH White individuals (1.6%) [adjusted cIR = 2.12, 95%CI = 1.16–3.89; p = 0.015]. Comorbidities significantly mediated the association between race and PJI, accounting for 26% of the total effect of race on PJI incidence. Interventions that increase access to high-quality treatments for comorbidities before and after TJA may reduce racial disparities in PJI.
Abstract As the number of total joint arthroplasties continues to rise, periprosthetic joint infection (PJI), a significant and devastating complication of total joint arthroplasty, may also increase. In PJI, bacterial biofilms are formed by causative pathogens surrounded by extracellular matrix with relatively dormant cells that can persist, resulting in a barrier against the host immune system and antibiotics. These biofilms not only contribute to the pathogenesis of PJI but also result in diagnostic challenges, antibiotic resistance, and PJI treatment failure. This review discusses the development of biofilms and key features associated with biofilm pathogenicity in PJI, current PJI diagnostic methods and their limitations, and current treatment options. Additionally, this article explores novel approaches to treat PJI, including targeting persister bacteria, immunotherapy, antimicrobial peptides, nanoparticles, and bacteriophage therapy. Biofilm eradication can also be achieved through enzymatic therapy, photodynamic therapy, and ultrasound. Finally, this review discusses novel techniques to prevent PJI, including improved irrigation solutions, smart implants with antimicrobial properties, inhibition of quorum sensing, and vaccines, which may revolutionize PJI management in the future by eradicating a devastating problem.
Identifying the design of a failed implant is a key step in the preoperative planning of revision total joint arthroplasty. Manual identification of the implant design from radiographic images is time-consuming and prone to error. Failure to identify the implant design preoperatively can lead to increased operating room time, more complex surgery, increased blood loss, increased bone loss, increased recovery time, and overall increased healthcare costs. In this study, we present a novel, fully automatic and interpretable approach to identify the design of total hip replacement (THR) implants from plain radiographs using deep convolutional neural network (CNN). CNN achieved 100% accuracy in the identification of three commonly used THR implant designs. Such CNN can be used to automatically identify the design of a failed THR implant preoperatively in just a few seconds, saving time and improving the identification accuracy. This can potentially improve patient outcomes, free practitioners' time, and reduce healthcare costs.
The primary aims of our study were to determine if hospital readmissions within one year following primary total joint arthroplasty (TJA) and their relative timing influence patients’ ability to achieve the two-year Patient-Reported Outcomes Measurement Information System (PROMIS) physical, PROMIS mental, and PROMIS Physical-Function-Short-Form-10a (SF-10a) minimal clinically important difference (MCID). This is a retrospective study conducted using data from a multi-institutional, arthroplasty registry. Only patients with paired patient-reported outcome measure (PROM) assessments (preoperatively and two years postoperatively) were included. Five separate readmission cohorts were formed: (1) any-cause readmission within one year, (2) any-cause readmission within 90 days, (3) non-index-surgery-related readmission within 90 days, (4) index-surgery-related readmission within one year, and (5) index-surgery-related readmission within 90 days. A propensity score match was used to match each of the patients to one of the 972 patients (1:1 basis) in the non-readmission group. The association between failure to achieve each of the three two-year MCIDs and Readmission status was analyzed using logistic regression. We found that all readmissions within one year and index-surgery-related readmissions within one year resulted in an increased risk of failure to achieve the two-year MCID across all three collected PROMs. Index surgery-related readmissions within 90 days (OR 3.24; 95% CI 1.05-11.05; p=0.048) sustained significantly different rates of two-year PROMIS physical MCID achievement compared to matched controls. Postoperative complications requiring readmission, particularly those related to the joint arthroplasty and those within 90 days of index surgery, significantly impact the ability to achieve the two-year MCID of PROMs.
➤ The consequences of undermanaged perioperative hyperglycemia are notable and can have a serious impact on adverse postoperative outcomes, especially surgical site infections and periprosthetic joint infections (PJIs). ➤ Preoperative screening of hemoglobin A1c with a goal threshold of <7.45% is ideal. ➤ There are a variety of risk factors that contribute to hyperglycemia that should be considered in the perioperative period, including glucocorticoid use, nutritional factors, patient-specific factors, anesthesia, and surgery. ➤ There are expected trends in the rise, peak, and fall of postoperative blood glucose levels, and identifying and treating hyperglycemia as swiftly as possible are the fundamental aims of treatment and improved glucose control. Performing frequent postoperative blood glucose monitoring (in the post-anesthesia care unit, on the day of surgery at 1700 and 2100 hours, and in the morning of postoperative day 1) should be considered to allow for the early detection of alterations in glucose metabolism. In addition, instituting a postoperative dietary restriction of carbohydrates should be considered. ➤ The use of insulin as a hypoglycemic agent in orthopaedic patients is relatively safe and is an effective means of controlling fluctuating blood glucose levels. Insulin therapy should be administered to treat hyperglycemia at ≥140 mg/dL when fasting and ≥180 mg/dL postprandially. Insulin therapy should be ceased at blood glucose levels of <110 mg/dL; however, monitoring for glycemic dysregulation should be continued. In all cases of complex diabetes, consultation with diabetes specialty services should be considered. ➤ The emerging use of technology, including continuous subcutaneous insulin pump therapy and continuous glucose monitoring, is an exciting area of further research and development as such technology can more immediately detect and correct aberrations in blood glucose levels.
