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2114. How to Predict Multi-Drug Resistance in Community-Acquired Urinary Tract Infection? Performance of an Easy and Simple New Scoring Model

BACKGROUND: Antibiotic resistance is a growing problem in community-acquired urinary tract infections (CAUTI) leading to significant challenges and costs in the healthcare system. We aimed to propose a reliable and an easy-to-use clinical prediction model to identify patients with multidrug-resistan...

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Autores principales: Ayed, Houda Ben, Koubaa, Makram, Hammami, Fatma, Marrakchi, Chakib, Jemaa, Tarak Ben, Maaloul, Imed, Dammak, Jamel, Jemaa, Mounir Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253906/
http://dx.doi.org/10.1093/ofid/ofy210.1770
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author Ayed, Houda Ben
Koubaa, Makram
Hammami, Fatma
Marrakchi, Chakib
Jemaa, Tarak Ben
Maaloul, Imed
Dammak, Jamel
Jemaa, Mounir Ben
author_facet Ayed, Houda Ben
Koubaa, Makram
Hammami, Fatma
Marrakchi, Chakib
Jemaa, Tarak Ben
Maaloul, Imed
Dammak, Jamel
Jemaa, Mounir Ben
author_sort Ayed, Houda Ben
collection PubMed
description BACKGROUND: Antibiotic resistance is a growing problem in community-acquired urinary tract infections (CAUTI) leading to significant challenges and costs in the healthcare system. We aimed to propose a reliable and an easy-to-use clinical prediction model to identify patients with multidrug-resistant (MDR) uro-pathogens. METHODS: We conducted a retrospective study including 824 patients with documented CAUTI diagnosed at an infectious diseases department during 2010–2017. Logistic-regression-based prediction scores were calculated based on variables independently associated with MDR. Sensitivities and specificities at various point cutoffs were studied and the determination of area under the receiver operating characteristic curve (AUROC) was performed. RESULTS: The median age of 824 patients with documented CAUTI was 54 years (IQR = [33–72 years]) and 542 cases (65.8%) were females. MDR germs were found in 372 cases (45.1%). Multivariate analysis showed that age ≥ 70 years (Adjusted OR = 2.5; 95% CI = [1.8–3.5]), diabetes (adjusted OR = 1.65; 95% CI = [1.19–2.3]), history of urinary tract surgery in the last past 12 months (adjusted OR = 4.5; 95% CI = [1.22–17]) and previous antimicrobial therapy in the last past 3 months (adjusted OR = 4.6; 95% CI = [3–7]) were the independent risk factors of MDR in CAUTI. The results of Hosmer-Lemshow chi-squared testing (χ(2) = 3.4; P = 0.49) were indicative of good calibration of the model. At a cut-off of ≥2, the score had an AUROC of 0.71, a good sensitivity (70.5%) but a lower specificity (60%), a PPV of 60%, an NPV of 70% and an overall diagnostic accuracy of 65%. When the cutoff was raised to 6, the sensitivity dropped to 43% and the specificity increased to 85%. CONCLUSION: Our study provided an insight into the clinical predictors of MDR in CAUTI. We developed a novel scoring system that can reliably identify patients likely to be harboring MDR uro-pathogens on hospital admission. DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-62539062018-11-28 2114. How to Predict Multi-Drug Resistance in Community-Acquired Urinary Tract Infection? Performance of an Easy and Simple New Scoring Model Ayed, Houda Ben Koubaa, Makram Hammami, Fatma Marrakchi, Chakib Jemaa, Tarak Ben Maaloul, Imed Dammak, Jamel Jemaa, Mounir Ben Open Forum Infect Dis Abstracts BACKGROUND: Antibiotic resistance is a growing problem in community-acquired urinary tract infections (CAUTI) leading to significant challenges and costs in the healthcare system. We aimed to propose a reliable and an easy-to-use clinical prediction model to identify patients with multidrug-resistant (MDR) uro-pathogens. METHODS: We conducted a retrospective study including 824 patients with documented CAUTI diagnosed at an infectious diseases department during 2010–2017. Logistic-regression-based prediction scores were calculated based on variables independently associated with MDR. Sensitivities and specificities at various point cutoffs were studied and the determination of area under the receiver operating characteristic curve (AUROC) was performed. RESULTS: The median age of 824 patients with documented CAUTI was 54 years (IQR = [33–72 years]) and 542 cases (65.8%) were females. MDR germs were found in 372 cases (45.1%). Multivariate analysis showed that age ≥ 70 years (Adjusted OR = 2.5; 95% CI = [1.8–3.5]), diabetes (adjusted OR = 1.65; 95% CI = [1.19–2.3]), history of urinary tract surgery in the last past 12 months (adjusted OR = 4.5; 95% CI = [1.22–17]) and previous antimicrobial therapy in the last past 3 months (adjusted OR = 4.6; 95% CI = [3–7]) were the independent risk factors of MDR in CAUTI. The results of Hosmer-Lemshow chi-squared testing (χ(2) = 3.4; P = 0.49) were indicative of good calibration of the model. At a cut-off of ≥2, the score had an AUROC of 0.71, a good sensitivity (70.5%) but a lower specificity (60%), a PPV of 60%, an NPV of 70% and an overall diagnostic accuracy of 65%. When the cutoff was raised to 6, the sensitivity dropped to 43% and the specificity increased to 85%. CONCLUSION: Our study provided an insight into the clinical predictors of MDR in CAUTI. We developed a novel scoring system that can reliably identify patients likely to be harboring MDR uro-pathogens on hospital admission. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2018-11-26 /pmc/articles/PMC6253906/ http://dx.doi.org/10.1093/ofid/ofy210.1770 Text en © The Author(s) 2018. 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
Ayed, Houda Ben
Koubaa, Makram
Hammami, Fatma
Marrakchi, Chakib
Jemaa, Tarak Ben
Maaloul, Imed
Dammak, Jamel
Jemaa, Mounir Ben
2114. How to Predict Multi-Drug Resistance in Community-Acquired Urinary Tract Infection? Performance of an Easy and Simple New Scoring Model
title 2114. How to Predict Multi-Drug Resistance in Community-Acquired Urinary Tract Infection? Performance of an Easy and Simple New Scoring Model
title_full 2114. How to Predict Multi-Drug Resistance in Community-Acquired Urinary Tract Infection? Performance of an Easy and Simple New Scoring Model
title_fullStr 2114. How to Predict Multi-Drug Resistance in Community-Acquired Urinary Tract Infection? Performance of an Easy and Simple New Scoring Model
title_full_unstemmed 2114. How to Predict Multi-Drug Resistance in Community-Acquired Urinary Tract Infection? Performance of an Easy and Simple New Scoring Model
title_short 2114. How to Predict Multi-Drug Resistance in Community-Acquired Urinary Tract Infection? Performance of an Easy and Simple New Scoring Model
title_sort 2114. how to predict multi-drug resistance in community-acquired urinary tract infection? performance of an easy and simple new scoring model
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6253906/
http://dx.doi.org/10.1093/ofid/ofy210.1770
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