Risk factors for development of aminoglycoside resistance among gram-negative rods.


Journal

American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
ISSN: 1535-2900
Titre abrégé: Am J Health Syst Pharm
Pays: England
ID NLM: 9503023

Informations de publication

Date de publication:
30 10 2019
Historique:
entrez: 31 10 2019
pubmed: 31 10 2019
medline: 11 3 2020
Statut: ppublish

Résumé

Development of scoring systems to predict the risk of aminoglycoside resistance and to guide therapy is described. Infections due to aminoglycoside-resistant gram-negative rods (AR-GNRs) are increasingly common and associated with adverse outcomes; selection of effective initial antibiotic therapy is necessary to reduce adverse consequences and shorten length of stay. To determine risk factors for AR-GNR recovery from culture, cases of GNR infection among patients admitted to 2 institutions in a major academic hospital system during the period 2011-2016 were retrospectively analyzed. Gentamicin and tobramycin resistance (GTR-GNR) and amikacin resistance (AmR-GNR) patterns were analyzed separately. A total of 26,154 GNR isolates from 12,516 patients were analyzed, 6,699 of which were GTR, and 2,467 of which were AmR. In multivariate analysis, risk factors for GTR-GNR were presence of weight loss, admission from another medical or long-term care facility, a hemoglobin level of <11 g/dL, receipt of any carbapenem in the prior 30 days, and receipt of any fluoroquinolone in the prior 30 days (C statistic, 0.63). Risk factors for AmR-GNR were diagnosis of cystic fibrosis, male gender, admission from another medical or long-term care facility, ventilation at any point prior to culture during the index hospitalization, receipt of any carbapenem in the prior 30 days, and receipt of any anti-MRSA agent in the prior 30 days (C statistic, 0.74). Multinomial and ordinal models demonstrated that the risk factors for the 2 resistance patterns differed significantly. A scoring system derived from the developed risk prediction models can be applied by providers to guide empirical antimicrobial therapy for treatment of GNR infections.

Identifiants

pubmed: 31665763
pii: 5609624
doi: 10.1093/ajhp/zxz201
pmc: PMC7170722
doi:

Substances chimiques

Aminoglycosides 0
Anti-Bacterial Agents 0
Carbapenems 0

Types de publication

Journal Article Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

1838-1847

Subventions

Organisme : NHLBI NIH HHS
ID : T32 HL007895
Pays : United States
Organisme : NIGMS NIH HHS
ID : U54 GM114833
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001881
Pays : United States

Informations de copyright

© American Society of Health-System Pharmacists 2019. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

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Auteurs

Stefan E Richter (SE)

Department of Cardiology, University of California, Los Angeles, and NIH BD2K Center of Excellence at UCLA, Los Angeles, CA.

Loren Miller (L)

Infectious Disease Clinical Outcome Research Unit, Los Angeles Biomedical Research Institute at Harbor-UCLA, Los Angeles, CA.

Jack Needleman (J)

Department of Health Policy and Management, University of California, Los Angeles, Los Angeles, CA.

Daniel Z Uslan (DZ)

Department of Infectious Disease, University of California, Los Angeles, Los Angeles, CA.

Douglas Bell (D)

Department of Internal Medicine, University of California, Los Angeles, Los Angeles, CA.

Karol Watson (K)

Department of Cardiology, University of California, Los Angeles, and NIH BD2K Center of Excellence at UCLA, Los Angeles, CA.

Romney Humphries (R)

Department of Pathology & Laboratory Medicine, University of California, Los Angeles, Los Angeles, CA, and Accelerate Diagnostics, Tucson, AZ.

James A McKinnell (JA)

Infectious Disease Clinical Outcome Research Unit Los Angeles Biomedical Research Institute at Harbor-UCLA, Los Angeles, CA.

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Classifications MeSH