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1462. Derivation of a Prediction Model for Risk of Drug-Resistant Urinary Tract Infection

BACKGROUND: Urinary tract infections (UTIs) are among the most common indications for antibiotic therapy. As antibiotic resistance continues to grow, it is critical to identify those at higher risk for drug-resistant (DR) UTIs to guide empiric therapy, improve clinical outcomes, and limit costs of c...

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Autores principales: Ali, Sabeen, Claeys, Kimberly C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6809021/
http://dx.doi.org/10.1093/ofid/ofz360.1326
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author Ali, Sabeen
Claeys, Kimberly C
author_facet Ali, Sabeen
Claeys, Kimberly C
author_sort Ali, Sabeen
collection PubMed
description BACKGROUND: Urinary tract infections (UTIs) are among the most common indications for antibiotic therapy. As antibiotic resistance continues to grow, it is critical to identify those at higher risk for drug-resistant (DR) UTIs to guide empiric therapy, improve clinical outcomes, and limit costs of care. The aim of this study was to identify risk factors for DR UTI and develop a risk scoring tool which could aid in empiric antibiotic prescribing. METHODS: Single-center retrospective pilot study of adult patients treated for UTI from August 1, 2015 to August 31, 2016. Patients who had asymptomatic bacteriuria, were pregnant within 4 months of admission, or had improperly collected urine cultures were excluded. DR was defined as phenotypic resistance to at least 1 agent in 3 or more antibiotic classes commonly used to treat UTIs. Risk factors for DR UTI were derived from previously published literature and multivariable logistic regression of individual patient data (IPD). Adjusted odds ratios (aORs) were developed by combining ORs from previous literature and IPD. A scoring tool was derived from weight-proportional integer-adjusted coefficients of the predictive model aORs. RESULTS: Risk factors were derived from 9 previously published studies and adapted using IPD (N = 77) and included: long-term care (aOR = 4.31), prior hospitalization (aOR = 1.8), previous antibiotics (aOR = 4.33), advanced age (aOR = 1.12), urinary catheterization (aOR = 2.2), immune suppression (aOR = 1.6), and male sex (aOR = 2.56). Previous DR UTI was forced into the model (OR = 1.1). Baseline incidence of DR UTI was 28.7%. A risk score from 1 to 20 was developed and applied to IPD and demonstrated an area under the receiver operator curve (AUROC) of 0.625 (95% CI 0.484–0.767). Removing sex from the score produced an AUROC of 0.64 (95% CI 0.497–783). A sensitivity analysis applying the score to only urinary isolates that exhibited resistance to third-generation cephalosporins (13.8%) produced similar results. CONCLUSION: Residence in long-term care and previous antibiotics were among the risk factors most closely associated with DR UTI. Considering cumulative risk scores may be useful in predicting DR UTI however the current study was hindered by a large degree of heterogeneity in previous literature. DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-68090212019-10-28 1462. Derivation of a Prediction Model for Risk of Drug-Resistant Urinary Tract Infection Ali, Sabeen Claeys, Kimberly C Open Forum Infect Dis Abstracts BACKGROUND: Urinary tract infections (UTIs) are among the most common indications for antibiotic therapy. As antibiotic resistance continues to grow, it is critical to identify those at higher risk for drug-resistant (DR) UTIs to guide empiric therapy, improve clinical outcomes, and limit costs of care. The aim of this study was to identify risk factors for DR UTI and develop a risk scoring tool which could aid in empiric antibiotic prescribing. METHODS: Single-center retrospective pilot study of adult patients treated for UTI from August 1, 2015 to August 31, 2016. Patients who had asymptomatic bacteriuria, were pregnant within 4 months of admission, or had improperly collected urine cultures were excluded. DR was defined as phenotypic resistance to at least 1 agent in 3 or more antibiotic classes commonly used to treat UTIs. Risk factors for DR UTI were derived from previously published literature and multivariable logistic regression of individual patient data (IPD). Adjusted odds ratios (aORs) were developed by combining ORs from previous literature and IPD. A scoring tool was derived from weight-proportional integer-adjusted coefficients of the predictive model aORs. RESULTS: Risk factors were derived from 9 previously published studies and adapted using IPD (N = 77) and included: long-term care (aOR = 4.31), prior hospitalization (aOR = 1.8), previous antibiotics (aOR = 4.33), advanced age (aOR = 1.12), urinary catheterization (aOR = 2.2), immune suppression (aOR = 1.6), and male sex (aOR = 2.56). Previous DR UTI was forced into the model (OR = 1.1). Baseline incidence of DR UTI was 28.7%. A risk score from 1 to 20 was developed and applied to IPD and demonstrated an area under the receiver operator curve (AUROC) of 0.625 (95% CI 0.484–0.767). Removing sex from the score produced an AUROC of 0.64 (95% CI 0.497–783). A sensitivity analysis applying the score to only urinary isolates that exhibited resistance to third-generation cephalosporins (13.8%) produced similar results. CONCLUSION: Residence in long-term care and previous antibiotics were among the risk factors most closely associated with DR UTI. Considering cumulative risk scores may be useful in predicting DR UTI however the current study was hindered by a large degree of heterogeneity in previous literature. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2019-10-23 /pmc/articles/PMC6809021/ http://dx.doi.org/10.1093/ofid/ofz360.1326 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Ali, Sabeen
Claeys, Kimberly C
1462. Derivation of a Prediction Model for Risk of Drug-Resistant Urinary Tract Infection
title 1462. Derivation of a Prediction Model for Risk of Drug-Resistant Urinary Tract Infection
title_full 1462. Derivation of a Prediction Model for Risk of Drug-Resistant Urinary Tract Infection
title_fullStr 1462. Derivation of a Prediction Model for Risk of Drug-Resistant Urinary Tract Infection
title_full_unstemmed 1462. Derivation of a Prediction Model for Risk of Drug-Resistant Urinary Tract Infection
title_short 1462. Derivation of a Prediction Model for Risk of Drug-Resistant Urinary Tract Infection
title_sort 1462. derivation of a prediction model for risk of drug-resistant urinary tract infection
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6809021/
http://dx.doi.org/10.1093/ofid/ofz360.1326
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