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Updated: Jan 4, 2026

Isolation and Identification of Waterborne Antibiotic-Resistant Bacteria and Molecular Characterization of their Antibiotic Resistance Genes
Published on: March 3, 2023
Risk factors for development of aminoglycoside resistance among gram-negative rods
Stefan E Richter1, Loren Miller2, Jack Needleman3
1Department of Cardiology, University of California, Los Angeles, and NIH BD2K Center of Excellence at UCLA, Los Angeles, CA.
Purpose:
Development of scoring systems to predict the risk of aminoglycoside resistance and to guide therapy is described.
Methods:
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.
Results:
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.
Conclusion:
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.
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