A decision support system for antibiotic prescription based on local cumulative antibiograms.

BACKGROUND: Local cumulative antibiograms are useful tools with which to select appropriate empiric or directed therapies when treating infectious diseases at a hospital. However, data represented in traditional antibiograms are static, incomplete and not well adapted to decision-making. METHODS: We propose a decision support method for empiric antibiotic therapy based on the Number Needed to Fail (NNF) measure. NNF indicates the number of patients that would need to be treated with a specific antibiotic for one to be inadequately treated. We define two new measures, Accumulated Efficacy and Weighted Accumulated Efficacy in order to determine the efficacy of an antibiotic. We carried out two experiments: the first during which there was a suspicion of infection and the patient had empiric therapy, and the second by considering patients with confirmed infection and directed therapy. The study was performed with 15,799 cultures with 356,404 susceptibility tests carried out over a four-year period. RESULTS: The most efficient empiric antibiotics are Linezolid and Vancomycin for blood samples and Imipenem and Meropenem for urine samples. In both experiments, the efficacies of recommended antibiotics are all significantly greater than the efficacies of the antibiotics actually administered (P < 0.001). The highest efficacy is obtained when considering 2 years of antibiogram data and 80% of the cumulated prevalence of microorganisms. CONCLUSION: This extensive study on real empiric therapies shows that the proposed method is a valuable alternative to traditional antibiograms as regards developing clinical decision support systems for antimicrobial stewardship.