LAUSR

Antibiograms are reports of local antimicrobial susceptibility patterns for common bacteria and yeast that are used to make empirical decisions for patient therapy and also to inform institution therapy guidelines. This study evaluates the impact of low isolate counts on the reliability of antibiograms, and suggests that more institutions should utilize multiple years of data to overcome this issue. ABSTRACT Antibiograms are cumulative reports of antimicrobial susceptibility results that are used to guide the selection of empirical antibiotic therapy. Although Clinical and Laboratory Standards Institute (CLSI) guidelines recommend including only organisms that have at least 30 isolates in an antibiogram, previous studies demonstrated that adherence to this recommendation is highly variable. This paper aims to model the impact of small sample sizes on expected levels of error in cumulative antibiograms by comparing percent susceptibility results for random samples to those of the larger, entire data set. The results demonstrate relatively high error rates when utilizing low numbers of isolates in cumulative antibiograms, and provide a discussion point for considering the appropriate number of isolates that could be utilized, and the impact of increasing isolate numbers by including multiple years of data. IMPORTANCE Antibiograms are reports of local antimicrobial susceptibility patterns for common bacteria and yeast that are used to make empirical decisions for patient therapy and also to inform institution therapy guidelines. This study evaluates the impact of low isolate counts on the reliability of antibiograms, and suggests that more institutions should utilize multiple years of data to overcome this issue.