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Demonstration of the Weighted-Incidence Syndromic Combination Antibiogram: An Empiric Prescribing Decision Aid

  • Courtney Hebert (a1), Jessica Ridgway (a2), Benjamin Vekhter (a3), Eric C. Brown (a4), Stephen G. Weber (a2) (a3) and Ari Robicsek (a3) (a4) (a5)...



Healthcare providers need a better empiric antibiotic prescribing aid than the traditional antibiogram, which supplies no information on the relative frequency of organisms recovered in a given infection and which is uninformative in situations where multiple antimicrobials are used or multiple organisms are anticipated. We aimed to develop and demonstrate a novel empiric prescribing decision aid.


This is a demonstration involving more than 9,000 unique encounters for abdominal-biliary infection (ABI) and urinary tract infection (UTI) to a large healthcare system with a fully integrated electronic health record (EHR).


We developed a novel method of displaying microbiology data called the weighted-incidence syndromic combination antibiogram (WISCA) for 2 clinical syndromes, ABI and UTI. The WISCA combines simple diagnosis and microbiology data from the EHR to (1) classify patients by syndrome and (2) determine, for each patient with a given syndrome, whether a given regimen (1 or more agents) would have covered all the organisms recovered for their infection. This allows data to be presented such that clinicians can see the probability that a particular regimen will cover a particular infection rather than the probability that a single drug will cover a single organism.


There were 997 encounters for ABI and 8,232 for UTI. A WISCA was created for each syndrome and compared with a traditional antibiogram for the same period.


Novel approaches to data compilation and display can overcome limitations to the utility of the traditional antibiogram in helping providers choose empiric antibiotics.


Corresponding author

3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210 (,


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Demonstration of the Weighted-Incidence Syndromic Combination Antibiogram: An Empiric Prescribing Decision Aid

  • Courtney Hebert (a1), Jessica Ridgway (a2), Benjamin Vekhter (a3), Eric C. Brown (a4), Stephen G. Weber (a2) (a3) and Ari Robicsek (a3) (a4) (a5)...


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