Database-driven computerized antibiotic decision support: novel use of expert antibiotic susceptibility rules embedded in a pathogen-antibiotic logic matrix.

To better serve an antibiotic guidance program, we hypothesized that the relatively few antibiotic susceptibility measurements conducted in the microbiology laboratory could be extended to predict antibiotic susceptibilities for all antibiotics on the hospital formulary using expert infectious disease logic. With the assistance of infectious disease specialists, we developed these logic rules and then applied them to 26,196 unique patient culture specimens and the accompanying 334,131 antibiotic susceptibility measurements generating 804,809 additional predicted bug-drug susceptibility data points. From the resulting data set, the antibiotic susceptibility profile for one pathogen, Streptococcus pneumoniae, is highlighted herein. We then incorporated the extended susceptibility profiles into a computerized antibiotic guidance program that matches current patients of interest with the positive cultures from past similar patients and calculates predicted effective antibiotic therapy. We conclude that this method successfully derives antibiotic predictions and merits further testing to evaluate its potential use in the hospital environment.