Risk Factors and trends associated with Bactrim non-susceptibility among patients with Carbapenem- resistant Acinetobacter baumannii identified through the Emerging Infections Program, 2012 to 2019 Público

Carmon, Clayton (Spring 2021)

Permanent URL: https://etd.library.emory.edu/concern/etds/dr26xz746?locale=es
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Abstract

Antibiotic-resistant bacteria have been identified as a serious threat to public health and can lead to high morbidity, mortality, and healthcare costs. Carbapenem-resistant Acinetobacter baumannii (CRAB) has been a pathogen of concern causing nosocomial infections since the early 1990s. The purpose of this study was to use surveillance data to identify potential risk factors for Bactrim non- susceptibility among CRAB isolates and to identify trends in Bactrim non-susceptibility from 2012-2019. Logistic regression modeling was used to determine the association between independent risk factors and Bactrim non-susceptibility among 1,134 CRAB cases identified through active laboratory-based surveillance between 2012 and 2019. A set of clinical risk factors were identified through literature review and assessed for statistically significant association with Bactrim non-susceptibility. Each risk factor was assessed independently in multivariable models that accounted for potential confounders. Odds ratios and respective 95% confidence intervals were calculated to determine the direction and strength of each risk factor. The final models identified significant associations between several risk factors and Bactrim non-susceptibility: Charleson Score of ≥4 versus ≤1 (OR 0.63, 95% CI 0.45, 0.88), Charleson Score 2 to 3 versus ≤1 (OR 0.70, 95% CI 0.51, 0.95), and pneumonia versus urinary tract infection (UTI) (OR 2.11, 95% CI 1.10, 4.02). The second model used to assess trends in Bactrim non- susceptibility showed that there was a slight increase in the odds of non-susceptibility to Bactrim among patients with CRAB, year over year; however, our model did not show a statistically significant increase during the study period: per one-year increase from 2012-2019 (OR 1.02, 95% CI 0.96, 1.08). Data from this study demonstrated that active surveillance could help elucidate relationships between risk factors and specific antimicrobial resistance among pathogens of concern, as well as potentially identify trends in non-susceptibility over time.

Table of Contents

TABLE OF CONTENTS

Chapter I: Background.............................................................................................................................................1

Chapter II: ManuscriptAbstract.....................................................................................................................................................................5 Introduction..............................................................................................................................................................6 Methods....................................................................................................................................................................7 Results.......................................................................................................................................................................11 Discussion.................................................................................................................................................................13 References................................................................................................................................................................18 Tables........................................................................................................................................................................20

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