Quantifying the effect of in-hospital antimicrobial use on the development of colistin-resistant Acinetobacter baumannii strains: a time series analysis.

OBJECTIVES: Colistin is currently considered an essential therapeutic option for the treatment of hospital-acquired infections caused by resistant isolates of Acinetobacter baumannii. In this study, conducted in a tertiary care hospital, the effect of previous detection rates and antimicrobial use on colistin-resistant A. baumannii strains was investigated.
METHODS: Susceptibility data for A. baumannii isolates and colistin use information were collected for 48 consecutive months (January 2014 to December 2017). ARIMA models were used to explore the time series of colistin use and resistance to A. baumannii. In addition, dynamic regression models were used to study the relationships between the use of antimicrobials (colistin, imipenem, meropenem, cefepime, ciprofloxacin) and colistin resistance.
RESULTS: The results of the univariate model showed a statistically significant positive association between colistin use and the detection rate of colistin-resistant A. baumannii (p=0.003). Moreover, a multivariate model confirmed the positive association of colistin use with the detection rate of colistin-resistant A. baumannii, also demonstrating statistically significant negative associations with imipenem (p=0.004) and meropenem use (p=0.005).
CONCLUSIONS: This study quantifies the effect of colistin use on the development of resistant strains. These findings can assist antimicrobial stewardship teams to elaborate their plans and predict the effect of their interventions. © European Association of Hospital Pharmacists 2022. No commercial re-use. See rights and permissions. Published by BMJ.