IV antibiotic administration and rate of antimicrobial resistance
Abstract:
Objective: The days of therapy (DOT) metric, used to estimate antimicrobial consumption, has some limitations. Days of antibiotic spectrum coverage (DASC), a novel metric, overcomes these limitations. We examined the difference between these 2 metrics of inpatient intravenous antimicrobial consumption in assessing antimicrobial stewardship efficacy and antimicrobial resistance using vector autoregressive (VAR) models with time-series analysis.
Methods: Differences between DOT and DASC were investigated at a tertiary-care center over 8 years using VAR models with 3 variables in the following order: (1) the monthly proportion of prospective audit and feedback (PAF) acceptance as an index of antimicrobial stewardship efficacy; (2) monthly DOT and DASC adjusted by 1,000 days present as indices of antimicrobial consumption; and (3) the monthly incidence of 5 organisms as an index of antimicrobial resistance.
Results: The Granger causality test, which evaluates whether incorporating lagged variables can help predict other variables, showed that PAF activity contributed to DOT and DASC, which, in turn, contributed to the incidence of drug-resistant P. aeruginosa. Notably, only DASC helped predict the incidence of drug-resistant Enterobacterales. Another VAR analysis demonstrated that a high proportion of PAF acceptance was accompanied by decreased DASC in a given month, whereas increased DASC was accompanied by an increased incidence of drug-resistant Enterobacterales, unlike with DOT.
Conclusions: The VAR models of PAF activity, antimicrobial consumption, and antimicrobial resistance suggested that DASC may more accurately reflect the impact of PAF on antimicrobial consumption and be superior to DOT for predicting the incidence of drug-resistant Enterobacterales.
Reference:
Murakami S, Akazawa M, Honda H. Difference between days of therapy and days of antibiotic spectrum coverage in an inpatient antimicrobial stewardship program: Vector autoregressive models for time-series analysis. Infect Control Hosp Epidemiol. 2023 Nov 8:1-8. doi: 10.1017/ice.2023.197. Epub ahead of print. PMID: 37937440.