171. How to measure antibiotic resistance using empiric therapy indices
Session: Poster Abstract Session: Antimicrobial Stewardship: Current State and Future Opportunities
Thursday, October 8, 2015
Room: Poster Hall
  • JosieHughes_EmpiricPoster.pdf (808.5 kB)
  • Background: Concise, standard, simple measures of the diversity of antibiotic resistance and its impact on health are needed to effectively communicate the burden of resistance to a wide audience, understand trends, evaluate interventions, and motivate investment.

    Methods: We developed two complementary indices of antibiotic resistance. The empiric resistance index (ERI) measures the coverage provided by available drugs for empiric therapy. The empiric options index (EOI) measures the value of multiple drugs, on the understanding that drug use will lead to resistance so more options are better. The indices account for the availability of treatment options and the relative importance of pathogens.

    Results: Scenarios show the behaviour and usefulness of the indices. In the ICUs of a large Toronto hospital the ERI remains high (98%) because a few drugs provide good coverage, but 50 to 60% of treatment potential measured by the EOI has been lost. Ceftazidime-avibactam and ceftolozane-tazobactam could increase the EOI by providing empiric coverage of Gram-negative infections. Carbapenemase (KPC)-producing Enterobacteriacea threaten empiric therapy (ERI=57-74%,EOI=1.9-2.8). Pandrug-resistant Acinetobacter poses less threat (ERI=95%,EOI=4.8-6) because it causes less disease. Increasing MRSA prevalence would have little impact (ERI=98%,EOI=4.8-5.6) because many Gram-positives are already resistant to β-lactams. Aminoglycoside resistance threatens the EOI (ERI=97%,EOI=3.7-4.9) because aminoglycosides cover Gram-negative infections.

    Conclusion: The ERI and EOI measure available empiric coverage and the value of multiple treatment options, providing a meaningful summary of resistance that can be calculated from cumulative antibiogram data. These indices can be used to understand trends, assess threats, and assess interventions.

    Josie Hughes, PhD, Centre for Disease Modelling, York University/Mt Sinai Hospital, Toronto, ON, Canada, Amy Hurford, PhD, Mathematics/Biology, Memorial University of Newfoundland, St. John's, NF, Canada, Rita Finley, MSc, Enteric Surveillance and Population Studies Division, Centre for Food-borne, Environmental and Zoonotic Infectious Diseases, Public Health Agency of Canada, Guelph, ON, Canada, David M. Patrick, MD, MHSc, School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada, Jianhong Wu, PhD, Center for Disease Modelling, York Institute for Health Research, York University, Toronto, ON, Canada and Andrew Morris, MD, SM, University of Toronto, Toronto, ON, Canada


    J. Hughes, None

    A. Hurford, None

    R. Finley, None

    D. M. Patrick, None

    J. Wu, None

    A. Morris, None

    Findings in the abstracts are embargoed until 12:01 a.m. PDT, Wednesday Oct. 7th with the exception of research findings presented at the IDWeek press conferences.