Feasibility of informing syndrome-level empiric antibiotic recommendations using publicly available antibiotic resistance datasets.

ABSTRACT: Background: Antibiotics are often prescribed empirically to treat infection syndromes before causative bacteria and their susceptibility to antibiotics are identified. Guidelines on empiric antibiotic prescribing are key to effective treatment of infection syndromes, and need to be informed by likely bacterial aetiology and antibiotic resistance patterns. We aimed to create a clinically-relevant composite index of antibiotic resistance for common infection syndromes to inform recommendations at the national level. Methods: To create our index, we used open-access antimicrobial resistance (AMR) surveillance datasets, including the ECDC Surveillance Atlas, CDDEP ResistanceMap, WHO GLASS and the newly-available Pfizer ATLAS dataset. We integrated these with data on aetiology of

SUBMITTER: Leclerc QJ 

PROVIDER: S-EPMC7327722 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature