OBJECTIVE: 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. DESIGN/ SETTING: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: Novel approaches to data compilation and display can overcome limitations to the utility of the traditional antibiogram in helping providers choose empiric antibiotics.
OBJECTIVE: 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. DESIGN/ SETTING: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: Novel approaches to data compilation and display can overcome limitations to the utility of the traditional antibiogram in helping providers choose empiric antibiotics.
Authors: Courtney Hebert; Yuan Gao; Protiva Rahman; Courtney Dewart; Mark Lustberg; Preeti Pancholi; Kurt Stevenson; Nirav S Shah; Erinn M Hade Journal: Antimicrob Agents Chemother Date: 2020-06-23 Impact factor: 5.191
Authors: Courtney M Dewart; Yuan Gao; Protiva Rahman; Awa Mbodj; Erinn M Hade; Kurt Stevenson; Courtney L Hebert Journal: Infect Control Hosp Epidemiol Date: 2018-07-23 Impact factor: 3.254
Authors: Josie S Hughes; Amy Hurford; Rita L Finley; David M Patrick; Jianhong Wu; Andrew M Morris Journal: BMJ Open Date: 2016-12-16 Impact factor: 2.692
Authors: Zafer Tandogdu; Evgenios T A Kakariadis; Kurt Naber; Florian Wagenlehner; Truls Erik Bjerklund Johansen Journal: PLoS One Date: 2019-04-25 Impact factor: 3.240