Prediction of Fluoroquinolone Resistance in Gram-Negative Bacteria Causing Bloodstream Infections

Author(s)

Seejil Dan, Department of Medicine, Palmetto Health Richland, Columbia, South Carolina, USA.
Ansal Shah, Department of Medicine, Palmetto Health Richland, Columbia, South Carolina, USA Department of Medicine, Division of Infectious Diseases, University of South Carolina School of Medicine
Julie Ann Justo, Department of Clinical Pharmacy and Outcomes Science, South Carolina College of Pharmacy, University of South Carolina, USA Department of Clinical Pharmacy, Palmetto Health RichlandFollow
P Brandon Bookstaver, University of South Carolina - ColumbiaFollow
Joseph Kohn, Department of Clinical Pharmacy, Palmetto Health Richland
Helmut Albrecht, Department of Medicine, Division of Infectious Diseases, University of South Carolina School of Medicine
Majdi N. Al-Hasan, Department of Medicine, Division of Infectious Diseases, University of South Carolina School of Medicine

Document Type

Article

Subject Area(s)

Aged; Anti-Bacterial Agents (therapeutic use); Area Under Curve; Bacteremia (diagnosis, drug therapy, microbiology); Case-Control Studies; Diabetes Mellitus (physiopathology); Drug Administration Schedule; Drug Resistance, Bacterial; Female; Fluoroquinolones (therapeutic use); Gram-Negative Bacteria (drug effects, growth & development, pathogenicity); Gram-Negative Bacterial Infections (diagnosis, drug therapy, microbiology); Humans; Logistic Models; Male; Middle Aged; Prognosis; Risk Factors; Sex Factors; South Carolina; Treatment Outcome

Abstract

Increasing rates of fluoroquinolone resistance (FQ-R) have limited empirical treatment options for Gram-negative infections, particularly in patients with severe beta-lactam allergy. This case-control study aims to develop a clinical risk score to predict the probability of FQ-R in Gram-negative bloodstream isolates. Adult patients with Gram-negative bloodstream infections (BSI) hospitalized at Palmetto Health System in Columbia, South Carolina, from 2010 to 2013 were identified. Multivariate logistic regression was used to identify independent risk factors for FQ-R. Point allocation in the fluoroquinolone resistance score (FQRS) was based on regression coefficients. Model discrimination was assessed by the area under receiver operating characteristic curve (AUC). Among 824 patients with Gram-negative BSI, 143 (17%) had BSI due to fluoroquinolone-nonsusceptible Gram-negative bacilli. Independent risk factors for FQ-R and point allocation in FQRS included male sex (adjusted odds ratio [aOR], 1.97; 95% confidence intervals [CI], 1.36 to 2.98; 1 point), diabetes mellitus (aOR, 1.54; 95% CI, 1.03 to 2.28; 1 point), residence at a skilled nursing facility (aOR, 2.28; 95% CI, 1.42 to 3.63; 2 points), outpatient procedure within 30 days (aOR, 3.68; 95% CI, 1.96 to 6.78; 3 points), prior fluoroquinolone use within 90 days (aOR, 7.87; 95% CI, 4.53 to 13.74; 5 points), or prior fluoroquinolone use within 91 to 180 days of BSI (aOR, 2.77; 95% CI, 1.17 to 6.16; 3 points). The AUC for both final logistic regression and FQRS models was 0.73. Patients with an FQRS of 0, 3, 5, or 8 had predicted probabilities of FQ-R of 6%, 22%, 39%, or 69%, respectively. The estimation of patient-specific risk of antimicrobial resistance using FQRS may improve empirical antimicrobial therapy and fluoroquinolone utilization in Gram-negative BSI.

Digital Object Identifier (DOI)

https://doi.org/10.1128/AAC.02728-15

Publication Info

Published in Antimicrobial Agents and Chemotherapy, Volume 60, Issue 4, 2016, pages 2265-2272.

Rights

Copyright © American Society for Microbiology, Antimicrobial Agents and Chemotherapy. https://doi.org/10.1128/AAC.02728-15, 2016.

APA Citation

Dan, S., Shah, A., Justo, J. A., Bookstaver, P. B., Kohn, J., Albrecht, H., & Al-Hasan, M. N. (2016). Prediction of fluoroquinolone resistance in gram-negative bacteria causing bloodstream infections. Antimicrobial Agents and Chemotherapy, 60(4), 2265–2272. https://doi.org/10.1128/aac.02728-15