Development of Predictive Models to Inform a Novel Risk Categorization Framework for Antibiotic Resistance in Escherichia coli-Caused Uncomplicated Urinary Tract Infection.

Autor:	Shields RK; Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA., Cheng WY; Analysis Group, Inc., Boston, Massachusetts, USA., Kponee-Shovein K; Analysis Group, Inc., Boston, Massachusetts, USA., Indacochea D; Analysis Group, Inc., Boston, Massachusetts, USA., Gao C; Analysis Group, Inc., Boston, Massachusetts, USA., Kuwer F; Analysis Group, Inc., Boston, Massachusetts, USA., Joshi AV; GSK, Collegeville, Pennsylvania, USA., Mitrani-Gold FS; GSK, Collegeville, Pennsylvania, USA., Schwab P; GSK, Collegeville, Pennsylvania, USA., Ferrinho D; GSK, Collegeville, Pennsylvania, USA., Mahendran M; Analysis Group, Inc., Boston, Massachusetts, USA., Pinheiro L; Analysis Group, Inc., Boston, Massachusetts, USA., Royer J; Analysis Group, Inc., Boston, Massachusetts, USA., Preib MT; GSK, Collegeville, Pennsylvania, USA., Han J; GSK, Collegeville, Pennsylvania, USA., Colgan R; Department of Family Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA.
Jazyk:	angličtina
Zdroj:	Clinical infectious diseases : an official publication of the Infectious Diseases Society of America [Clin Infect Dis] 2024 Aug 16; Vol. 79 (2), pp. 295-304.
DOI:	10.1093/cid/ciae171
Abstrakt:	Background: In clinical practice, challenges in identifying patients with uncomplicated urinary tract infections (uUTIs) at risk of antibiotic nonsusceptibility may lead to inappropriate prescribing and contribute to antibiotic resistance. We developed predictive models to quantify risk of nonsusceptibility to 4 commonly prescribed antibiotic classes for uUTI, identify predictors of nonsusceptibility to each class, and construct a corresponding risk categorization framework for nonsusceptibility. Methods: Eligible females aged ≥12 years with Escherichia coli-caused uUTI were identified from Optum's de-identified Electronic Health Record dataset (1 October 2015-29 February 2020). Four predictive models were developed to predict nonsusceptibility to each antibiotic class and a risk categorization framework was developed to classify patients' isolates as low, moderate, and high risk of nonsusceptibility to each antibiotic class. Results: Predictive models were developed among 87 487 patients. Key predictors of having a nonsusceptible isolate to ≥3 antibiotic classes included number of previous UTI episodes, prior β-lactam nonsusceptibility, prior fluoroquinolone treatment, Census Bureau region, and race. The risk categorization framework classified 8.1%, 14.4%, 17.4%, and 6.3% of patients as having isolates at high risk of nonsusceptibility to nitrofurantoin, trimethoprim-sulfamethoxazole, β-lactams, and fluoroquinolones, respectively. Across classes, the proportion of patients categorized as having high-risk isolates was 3- to 12-fold higher among patients with nonsusceptible isolates versus susceptible isolates. Conclusions: Our predictive models highlight factors that increase risk of nonsusceptibility to antibiotics for uUTIs, while the risk categorization framework contextualizes risk of nonsusceptibility to these treatments. Our findings provide valuable insight to clinicians treating uUTIs and may help inform empiric prescribing in this population. Competing Interests: Potential conflicts of interest. R. K. S. is a paid consultant to GSK. W. Y. C., K. K.-S., D. I., F. K., C. G., M. M., L. P., and J. R. are employees of Analysis Group, Inc., a consultancy that received funding from GSK to conduct this study. A. V. J., F. S. M.-G., P. S., D. F., M. T. P., and J. H. are employees of and shareholders in GSK. R. C. has served as a paid consultant to GSK. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed. (© The Author(s) 2024. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)
Databáze:	MEDLINE
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