Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source
Abstract 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 cr...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-05-01
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| Series: | Communications Medicine |
| Online Access: | https://doi.org/10.1038/s43856-023-00289-7 |
| _version_ | 1827952621485293568 |
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| author | Stacey S. Cherny Michal Chowers Uri Obolski |
| author_facet | Stacey S. Cherny Michal Chowers Uri Obolski |
| author_sort | Stacey S. Cherny |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T14:00:30Z |
| format | Article |
| id | doaj.art-dfcdded941e74b029128b9376a95d308 |
| institution | Directory Open Access Journal |
| issn | 2730-664X |
| language | English |
| last_indexed | 2024-04-09T14:00:30Z |
| publishDate | 2023-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Medicine |
| spelling | doaj.art-dfcdded941e74b029128b9376a95d3082023-05-07T11:22:01ZengNature PortfolioCommunications Medicine2730-664X2023-05-01311710.1038/s43856-023-00289-7Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample sourceStacey S. Cherny0Michal Chowers1Uri Obolski2School of Public Health, Tel Aviv UniversityMeir Medical CenterSchool of Public Health, Tel Aviv UniversityAbstract 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.https://doi.org/10.1038/s43856-023-00289-7 |
| spellingShingle | Stacey S. Cherny Michal Chowers Uri Obolski Bayesian network modeling of patterns of antibiotic cross-resistance by bacterial sample source Communications Medicine |
| 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 |
| url | https://doi.org/10.1038/s43856-023-00289-7 |
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