Poster Presentation BacPath 13: Molecular Analysis of Bacterial Pathogens Conference 2015

Impact of cefepime treatment on antibiotic resistance patterns in the Enterobacteriaceae (#178)

Carola Venturini 1 2 , Andrew Ginn 1 2 , Sally Partridge 1 2 , Guy Tsafnat 3 , Jon Iredell 1 2
  1. Centre for Infectious Diseases and Microbiology, Westmead Millennium Institute, Westmead, NSW, Australia
  2. Institute of Emerging Infectious Diseases and Biosecurity Marie Bashir Institute, The University of Sydney, Sydney, NSW, Australia
  3. Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia

Multiple antibiotic resistance in Gram-negative bacteria causing life-threatening sepsis, heavily impacts the ICU where antibiotic usage is high and patients more vulnerable, leading to increased morbidity and mortality.1 Timely antibiotic intervention is critical in improving survival,2 and preservation of susceptibility to broad-spectrum antibiotics for effective empiric therapy is paramount. Controlled antibiotic usage (“stewardship”) has thus become a primary public health agenda.3 Different antibiotics are known to have different effects on the composition of the gut microbiome,4, 5 and better understanding of these effects is needed to guide stewardship decisions. Cefepime has been advocated as effective empiric therapy but its in vivo efficacy and impact on resistance are still disputed.6,7

In this study, we compared cefepime treatment with anti-pseudomonal penicillin combinations (APP-β) by determining Enterobacteriaceae colonization and resistance rates at admission and after antibiotic therapy in 206 ICU patients. We also characterized the E. coli population in a subset of 12 patients by sequencing (paired-end MiSeq) representative pools of perineal clones before and after cefepime therapy. Reads were run against the NCBI database (BLAST8) and genetic features linked to transmissible resistance were carefully annotated (Attacca9).

Cefepime treatment was the main driver of both acquisition of resistant Enterobacteriaceae after therapy (p=0.027) and increased APP-β resistance (p=0.027). Accordingly, we observed that in the 'after' cefepime E. coli sample, the frequency of specific genes with strong links to multiple resistance (e.g. strAB, sul2) increased, while other genes (e.g. blaCMY-2) and markers of transmissible resistance (e.g. IncI1rep) were detected exclusively in the 'after' sample.

This study shows that in vivo APP-β is superior to cefepime in curtailing resistance development in Gram-negative Enterobacteriaceae. For truly effective stewardship intervention, the type of antibiotics used must be carefully considered due to possible implications for both the transmission of antibiotic resistance determinants and establishment of resistant populations.

  1. WHO (2014) Antimicrobial resistance: global report on surveillance 2014 http://www.who.int/drugresistance/documents/surveillancereport/en/
  2. Kumar et al. (2006) Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Crit Care Med. 34:1589-96.
  3. National Health and Hospitals Network Act 2011 Australian Commission on Quality and Safety in Health Care Commonwealth of Australia
  4. Jernberg C et al. (2007) Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. ISME J. 1:56-66.
  5. Rahal JJ et al. (1998) Class restriction of cephalosporin use to control total cephalosporin resistance in nosocomial Klebsiella. Jama. 280:1233-7.
  6. Ginn AN et al. (2012) The ecology of antibiotic use in the ICU: homogeneous prescribing of cefepime but not tazocin selects for antibiotic resistant infection. PLoS One. 7(6):e38719.
  7. Endimiani et al. (2008) Cefepime: a reappraisal in an era of increasing antimicrobial resistance. Expert Rev Anti Infect Ther. 6:805-24.
  8. Altschul SF et al. (1990) Basic local alignment search tool. J Mol Biol. 215:403-10.
  9. Tsafnat G et al. (2009) Context-driven discovery of gene cassettes in mobile integrons using a computational grammar. BMC Bioinformatics. 10:281.