What Is the Rate of Antimicrobial Resistance of a Prosthetic Joint Infection in a Major Orthopaedic Centre?
Belgin CoşkunMuge AyhanMerve BozerHalil Ibrahim OzaslanMetin DoganAhmad Shoib SharqzadMustafa AkkayaPublished in: Antibiotics (Basel, Switzerland) (2024)
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.
Keyphrases
- gram negative
- multidrug resistant
- antimicrobial resistance
- escherichia coli
- klebsiella pneumoniae
- staphylococcus aureus
- case report
- total knee arthroplasty
- pseudomonas aeruginosa
- ejection fraction
- big data
- minimally invasive
- newly diagnosed
- total hip arthroplasty
- prognostic factors
- biofilm formation
- combination therapy
- methicillin resistant staphylococcus aureus
- smoking cessation
- candida albicans
- drug induced
- data analysis