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Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source
BACKGROUND: Antimicrobial resistance is a major healthcare burden, aggravated when it extends to multiple drugs. While cross-resistance is well-studied experimentally, it is not the case in clinical settings, and especially not while considering confounding. Here, we estimated patterns of cross-resi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154291/ https://www.ncbi.nlm.nih.gov/pubmed/37130943 http://dx.doi.org/10.1038/s43856-023-00289-7 |
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author | Cherny, Stacey S. Chowers, Michal Obolski, Uri |
author_facet | Cherny, Stacey S. Chowers, Michal Obolski, Uri |
author_sort | Cherny, Stacey S. |
collection | PubMed |
description | BACKGROUND: Antimicrobial resistance is a major healthcare burden, aggravated when it extends to multiple drugs. While cross-resistance is well-studied experimentally, it is not the case in clinical settings, and especially not while considering confounding. Here, we estimated patterns of cross-resistance from clinical samples, while controlling for multiple clinical confounders and stratifying by sample sources. METHODS: We employed additive Bayesian network (ABN) modelling to examine antibiotic cross- resistance in five major bacterial species, obtained from different sources (urine, wound, blood, and sputum) in a clinical setting, collected in a large hospital in Israel over a 4-year period. Overall, the number of samples available were 3525 for E coli, 1125 for K pneumoniae, 1828 for P aeruginosa, 701 for P mirabilis, and 835 for S aureus. RESULTS: Patterns of cross-resistance differ across sample sources. All identified links between resistance to different antibiotics are positive. However, in 15 of 18 instances, the magnitudes of the links are significantly different between sources. For example, E coli exhibits adjusted odds ratios of gentamicin-ofloxacin cross-resistance ranging from 3.0 (95%CI [2.3,4.0]) in urine samples to 11.0 (95%CI [5.2,26.1]) in blood samples. Furthermore, we found that for P mirabilis, the magnitude of cross-resistance among linked antibiotics is higher in urine than in wound samples, whereas the opposite is true for K pneumoniae and P aeruginosa. CONCLUSIONS: Our results highlight the importance of considering sample sources when assessing likelihood of antibiotic cross-resistance. The information and methods described in our study can refine future estimation of cross-resistance patterns and facilitate determination of antibiotic treatment regimens. |
format | Online Article Text |
id | pubmed-10154291 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-101542912023-05-04 Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source Cherny, Stacey S. Chowers, Michal Obolski, Uri Commun Med (Lond) Article BACKGROUND: Antimicrobial resistance is a major healthcare burden, aggravated when it extends to multiple drugs. While cross-resistance is well-studied experimentally, it is not the case in clinical settings, and especially not while considering confounding. Here, we estimated patterns of cross-resistance from clinical samples, while controlling for multiple clinical confounders and stratifying by sample sources. METHODS: We employed additive Bayesian network (ABN) modelling to examine antibiotic cross- resistance in five major bacterial species, obtained from different sources (urine, wound, blood, and sputum) in a clinical setting, collected in a large hospital in Israel over a 4-year period. Overall, the number of samples available were 3525 for E coli, 1125 for K pneumoniae, 1828 for P aeruginosa, 701 for P mirabilis, and 835 for S aureus. RESULTS: Patterns of cross-resistance differ across sample sources. All identified links between resistance to different antibiotics are positive. However, in 15 of 18 instances, the magnitudes of the links are significantly different between sources. For example, E coli exhibits adjusted odds ratios of gentamicin-ofloxacin cross-resistance ranging from 3.0 (95%CI [2.3,4.0]) in urine samples to 11.0 (95%CI [5.2,26.1]) in blood samples. Furthermore, we found that for P mirabilis, the magnitude of cross-resistance among linked antibiotics is higher in urine than in wound samples, whereas the opposite is true for K pneumoniae and P aeruginosa. CONCLUSIONS: Our results highlight the importance of considering sample sources when assessing likelihood of antibiotic cross-resistance. The information and methods described in our study can refine future estimation of cross-resistance patterns and facilitate determination of antibiotic treatment regimens. Nature Publishing Group UK 2023-05-02 /pmc/articles/PMC10154291/ /pubmed/37130943 http://dx.doi.org/10.1038/s43856-023-00289-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Cherny, Stacey S. Chowers, Michal Obolski, Uri Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source |
title | Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source |
title_full | Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source |
title_fullStr | Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source |
title_full_unstemmed | Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source |
title_short | Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source |
title_sort | bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10154291/ https://www.ncbi.nlm.nih.gov/pubmed/37130943 http://dx.doi.org/10.1038/s43856-023-00289-7 |
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