Classification and management options for prosthetic joint infection

Microbiology of Prosthetic Joint Infections: A Retrospective Study of an Italian Orthopaedic Hospital

The most frequent cause of periprosthetic infections (PJIs) is intraoperative contamination; hence, antibiotic prophylaxis plays a crucial role in prevention. Modifications to standard prophylaxis can be considered if there is a high incidence of microorganisms resistant to current protocols. To date, very few studies regarding microbial etiology have been published in Italy. In this single-center, retrospective study conducted at IRCCS Ospedale Galeazzi-Sant'Ambrogio in Milan, we analyzed hip, knee, and shoulder PJIs in patients undergoing first implantation between 1 January 17 and 31 December 2021. The primary aim was to derive a local microbiological case history. The secondary aim was to evaluate the adequacy of preoperative antibiotic prophylaxis in relation to the identified bacteria. A total of 57 PJIs and 65 pathogens were identified: 16 S. aureus, 15 S. epidermidis, and 10 other coagulase-negative staphylococci (CoNS), which accounted for 63% of the isolations. A total of 86.7% of S. epidermidis were methicillin-resistant (MRSE). In line with other case reports, we found a predominance of staphylococcal infections, with a lower percentage of MRSA than the Italian average, while we found a high percentage of MRSE. We estimated that 44.6% of the bacteria isolated were resistant to cefazolin, our standard prophylaxis. These PJIs could be prevented by using glycopeptide alone or in combination with cefazolin, but the literature reports conflicting results regarding the adequacy of such prophylaxis. In conclusion, our study showed that in our local hospital, our standard antibiotic prophylaxis is ineffective for almost half of the cases, highlighting the importance of defining specific antibiotic guidelines based on the local bacterial prevalence of each institution.

What Is the Rate of Antimicrobial Resistance of a Prosthetic Joint Infection in a Major Orthopaedic Centre?

Periprosthetic joint infections (PJIs) are important factors in decreasing the success of hip and knee arthroplasties. It is a necessity to explore the epidemiological data and develop applications for rational antibiotic use, to address future infection control concerns. We aimed to investigate the microorganisms that were responsible and the related antibiograms in 121 patients with PJI, who were managed by two-stage revision surgery. Patients' data records, demographics, comorbidities, sites of arthroplasty, synovial fluid and deep tissue culture results and antibiotic treatment were summarized on a standardized case report form. There were 43 (35.5%) culture-negative PJI cases and 12 (9.9%) polymicrobial growths. The causative pathogens included Gram-positive (50.4%) and Gram-negative microorganisms (23.1%) and fungi (0.8%). Methicillin resistance was 64.3% for S. aureus and 89.5% for coagulase-negative staphylococcus (CoNS). The extended spectrum beta lactamase (ESBL) rate for Enterobacteriaceae was 68.4%. This study shows that antibiotic resistance is encountered in more than half of the cases, which is valid for all microorganisms most common in PJI. The success of treatment decreases significantly in cases where antibiotic-resistant microorganisms are isolated or in cases where the culture is negative.

Diagnostic value of magnetic resonance imaging for patients with periprosthetic joint infection: a systematic review

PURPOSE

The purpose of this study was to provide a critical systematic review of the role of magnetic resonance imaging (MRI) as a noninvasive method to assess periprosthetic joint infections (PJIs).

METHODS

The electronic databases PubMed and EMBASE were searched, since their inception up to March 27, 2022. The included studies evaluated the reproducibility and accuracy of MRI features to diagnose PJIs. The article quality assessment was conducted by the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2).

RESULTS

Among 1909 studies identified in the initial search, 8 studies were eligible for final systematic review. The included studies evaluated the reproducibility and accuracy of MRI features to diagnose PJIs. Seven of 8 studies showed good to excellent reliability, but only one article among them in which accuracy was evaluated had a low risk of bias. The intraclass correlation coefficient (ICC) and Cohen coefficient (κ) varied between 0.44 and 1.00. The accuracy varied between 63.9% and 94.4%. Potential MRI features, such as lamellated hyperintense synovitis, edema, fluid collection, or lymphadenopathy, might be valuable for diagnosing PJIs.

CONCLUSION

The quality of the evidence regarding the role of MRI for PJIs diagnosis was low. There is preliminary evidence that MRI has a noteworthy value of distinguishing suspected periprosthetic joint infection in patients with total knee arthroplasty or total hip arthroplasty, but the definition of specific MRI features related to PJIs diagnosis lacks consensus and standardization. Large-scale studies with robust quality were required to help make better clinical decisions in the future.

Emerging Technologies in Diagnosing Periprosthetic Joint Infections

Periprosthetic joint infection (PJI) is a well-known serious complication following joint replacement surgeries and is responsible for high failure rates of implanted devices. Any delay in the diagnosis can compromise treatment success, putting a huge burden on the patients' wellness and healthcare systems. Diagnosing PJIs is quite complex as there is still no gold standard test to reach the definitive diagnosis in a timely manner. A number of laboratory tests and radiological imaging inventions have evolved in the past few years, requiring consistent updates of the available guidelines to keep up with the latest advances in the field. This article highlights the recent advances in diagnosing PJIs and discusses their validity for use in clinical practice.

First report of a prosthetic joint infection with Fannyhessea (Atopobium) vaginae

This case describes a 77-year-old woman with dysregulated type II diabetes, presenting with a prosthetic joint infection and bacteremia. Computed tomography (CT) of the pelvis and sacrum revealed manifest periprosthetic collections, suggestive of a septic arthritis with loosening of the hip prosthesis. Synovial fluid grew Fannyhessea vaginae, identified by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS). To our knowledge, this is the first report of a prosthetic joint infection due to this organism.

Predicting the Exception-CRP and Primary Hip Arthroplasty

BACKGROUND

While primary hip arthroplasty is the most common operative procedure in orthopedic surgery, a periprosthetic joint infection is its most severe complication. Early detection and prediction are crucial. In this study, we aimed to determine the value of postoperative C-reactive protein (CRP) and develop a formula to predict this rare, but devastating complication.

METHODS

We retrospectively evaluated 708 patients with primary hip arthroplasty. CRP, white blood cell count (WBC), and several patient characteristics were assessed for 20 days following the operative procedure.

RESULTS

Eight patients suffered an early acute periprosthetic infection. The maximum CRP predicted an infection with a sensitivity and specificity of 75% and 56.9%, respectively, while a binary logistic regression reached values of 75% and 80%. A multinominal logistic regression, however, was able to predict an early infection with a sensitivity and specificity of 87.5% and 78.9%. With a one-phase decay, 71.6% of the postoperative CRP-variance could be predicted.

CONCLUSION

To predict early acute periprosthetic joint infection after primary hip arthroplasty, a multinominal logistic regression is the most promising approach. Including five parameters, an early infection can be predicted on day 5 after the operative procedure with 87.5% sensitivity, while it can be excluded with 78.9% specificity.