Abstract
An important function of a hospital’s Infectious Disease and Pharmacy programs is to review and compare the most recent antibiogram with that of the previous year to determine if significant changes in antibiotic susceptibility results are noted and to communicate this information and its consequences to the medical staff. However, there are currently no formal analytical (decision-making) models in use to determine if the rate of resistance to an antibiotic from one year to the next has significantly changed more or less than one would expect due to sampling error and test reliability. The purpose of this article, therefore, is to demonstrate the utility of using a well-established and simple nonparametric statistical technique (chi-square) for analyzing annual variations in cumulative antibiogram data and to determine whether such variations are significantly different from chance and to what to degree. The chi-square model outlined here is a simple, practical, quick, low burden and easy to understand and execute approach that greatly improves the analysis of antibiogram data and decisionmaking by practitioners. More work and research is needed to develop additional inferential statistical methods and models that can be applied to antibiogram data.
Highlights
The Clinical and Laboratory Standards Institute (CLSI, formerly the National Committee for ClinicalLaboratory Standards) defines an antibiogram as an overall profile of antimicrobial susceptibility results of a microbial species to a battery of antimicrobial agents[1]
An important function of a hospital’s Infectious Disease and Pharmacy programs is to review and compare the most recent antibiogram data with that of the previous year to determine if significant changes in antibiotic susceptibility results are noted
Using a color and arrow scheme allows anyone looking at the data to quickly gauge both the direction and magnitude of the antibiogram variations
Summary
Because one of the cells bacterial species (as is the case for ceftazidime in in Table 1 (Expected cases/Resistant) is small and treating P. aeruginosa infections), it seems appropriate because there is only one degree of freedom, the Yates to set alpha at .05 (vs .01) and to tolerate a higher risk correction ( controversial) is applied to this of sampling error when the decision-making preference case to reduce the chance of artificially increasing χ2 thereby making it more difficult to establish significance and reducing Type I error (accepting a false hypothesis) in this particular type of decisionis to accept and act on a “slight or close” variation between the expected and observed antibogram values.
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