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714. Genotypic Resistance in Acinetobacter as a Predictor for Patient Outcomes
BACKGROUND: Infections caused by Acinetobacter baumannii are frequently difficult to treat with currently available antibiotics. Oxacillinases (blaOXA-23), beta-lactamase (blaTEM-1D) are known to contribute to antibiotic resistance. The purpose of this study was to identify if resistance genes such...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10677179/ http://dx.doi.org/10.1093/ofid/ofad500.776 |
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author | Jinadatha, Chetan Dhar, Sorabh Coppin, John David Navarathna, Thanuri Kaye, Keith S Choi, Hosoon Chatterjee, Piyali |
author_facet | Jinadatha, Chetan Dhar, Sorabh Coppin, John David Navarathna, Thanuri Kaye, Keith S Choi, Hosoon Chatterjee, Piyali |
author_sort | Jinadatha, Chetan |
collection | PubMed |
description | BACKGROUND: Infections caused by Acinetobacter baumannii are frequently difficult to treat with currently available antibiotics. Oxacillinases (blaOXA-23), beta-lactamase (blaTEM-1D) are known to contribute to antibiotic resistance. The purpose of this study was to identify if resistance genes such as blaOXA-23 and/or blaTEM-1D contribute to adverse patient outcomes such as prolonged infection leading to increased duration of stay and mortality in hospitalized patients. METHODS: Clinical patient isolates were collected from two acute care hospitals, a 383-bed, and a 248-bed hospital in the Detroit metropolitan area. A total of 115 isolates samples and with complete clinical data sets were collected from 2 medical ICUs, 2 surgical ICU wards and 12 non-ICU surgical wards between 2017-2021. Resistance genes were analyzed using Resfinder (version 4.0) following whole genome sequencing (Illumina, NextSeq). Separate Bayesian regression models were used for the outcomes of interest: length of stay (LOS) and 30-day mortality, for each gene, blaOXA-23 and blaTEM-1D, as a predictor and adjusting for age and sex, using negative binomial models for LOS and logistic regression for mortality. Full models were fit to each outcome that included the interaction of both genes and adjusted for age, gender, hospital unit, respiratory infection, diabetes, COPD, hypertension, and renal dysfunction. RESULTS: The patient clinical characteristics between the resistant and susceptible Acinetobacter groups were matched (Table1). There was little evidence for the genes affecting either 30-day mortality or LOS for the full models. There was some evidence for increased odds of mortality with the presence of blaOXA-23, 0.75 (95% uncertainty interval: -0.06, 1.60), or both blaOXA-23 and blaTEM-1D, 0.85 (-0.2, 2.0) but not at the 95% level (Figure 1, Table 2). There was little evidence for an effect for the genes on LOS. [Figure: see text] [Figure: see text] [Figure: see text] CONCLUSION: Although there was a trend towards resistance genes affecting outcomes, the full analysis indicated no relationship between resistance genes and overall outcomes. Other factors such as advanced age, and comorbidities may be contributing to the mortality. In the future, factors that contribute to adverse clinical outcomes using a larger cohort will be explored. DISCLOSURES: Keith S. Kaye, MD, MPH, Abbvie: Advisor/Consultant|Abbvie: Honoraria|Entasis: Advisor/Consultant|Entasis: Honoraria|GSK: Advisor/Consultant|GSK: Honoraria|Merck: Advisor/Consultant|Merck: Honoraria|Shionogi: Advisor/Consultant|Shionogi: Honoraria|VenatoRx: Advisor/Consultant|VenatoRx: Honoraria |
format | Online Article Text |
id | pubmed-10677179 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-106771792023-11-27 714. Genotypic Resistance in Acinetobacter as a Predictor for Patient Outcomes Jinadatha, Chetan Dhar, Sorabh Coppin, John David Navarathna, Thanuri Kaye, Keith S Choi, Hosoon Chatterjee, Piyali Open Forum Infect Dis Abstract BACKGROUND: Infections caused by Acinetobacter baumannii are frequently difficult to treat with currently available antibiotics. Oxacillinases (blaOXA-23), beta-lactamase (blaTEM-1D) are known to contribute to antibiotic resistance. The purpose of this study was to identify if resistance genes such as blaOXA-23 and/or blaTEM-1D contribute to adverse patient outcomes such as prolonged infection leading to increased duration of stay and mortality in hospitalized patients. METHODS: Clinical patient isolates were collected from two acute care hospitals, a 383-bed, and a 248-bed hospital in the Detroit metropolitan area. A total of 115 isolates samples and with complete clinical data sets were collected from 2 medical ICUs, 2 surgical ICU wards and 12 non-ICU surgical wards between 2017-2021. Resistance genes were analyzed using Resfinder (version 4.0) following whole genome sequencing (Illumina, NextSeq). Separate Bayesian regression models were used for the outcomes of interest: length of stay (LOS) and 30-day mortality, for each gene, blaOXA-23 and blaTEM-1D, as a predictor and adjusting for age and sex, using negative binomial models for LOS and logistic regression for mortality. Full models were fit to each outcome that included the interaction of both genes and adjusted for age, gender, hospital unit, respiratory infection, diabetes, COPD, hypertension, and renal dysfunction. RESULTS: The patient clinical characteristics between the resistant and susceptible Acinetobacter groups were matched (Table1). There was little evidence for the genes affecting either 30-day mortality or LOS for the full models. There was some evidence for increased odds of mortality with the presence of blaOXA-23, 0.75 (95% uncertainty interval: -0.06, 1.60), or both blaOXA-23 and blaTEM-1D, 0.85 (-0.2, 2.0) but not at the 95% level (Figure 1, Table 2). There was little evidence for an effect for the genes on LOS. [Figure: see text] [Figure: see text] [Figure: see text] CONCLUSION: Although there was a trend towards resistance genes affecting outcomes, the full analysis indicated no relationship between resistance genes and overall outcomes. Other factors such as advanced age, and comorbidities may be contributing to the mortality. In the future, factors that contribute to adverse clinical outcomes using a larger cohort will be explored. DISCLOSURES: Keith S. Kaye, MD, MPH, Abbvie: Advisor/Consultant|Abbvie: Honoraria|Entasis: Advisor/Consultant|Entasis: Honoraria|GSK: Advisor/Consultant|GSK: Honoraria|Merck: Advisor/Consultant|Merck: Honoraria|Shionogi: Advisor/Consultant|Shionogi: Honoraria|VenatoRx: Advisor/Consultant|VenatoRx: Honoraria Oxford University Press 2023-11-27 /pmc/articles/PMC10677179/ http://dx.doi.org/10.1093/ofid/ofad500.776 Text en © The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Abstract Jinadatha, Chetan Dhar, Sorabh Coppin, John David Navarathna, Thanuri Kaye, Keith S Choi, Hosoon Chatterjee, Piyali 714. Genotypic Resistance in Acinetobacter as a Predictor for Patient Outcomes |
title | 714. Genotypic Resistance in Acinetobacter as a Predictor for Patient Outcomes |
title_full | 714. Genotypic Resistance in Acinetobacter as a Predictor for Patient Outcomes |
title_fullStr | 714. Genotypic Resistance in Acinetobacter as a Predictor for Patient Outcomes |
title_full_unstemmed | 714. Genotypic Resistance in Acinetobacter as a Predictor for Patient Outcomes |
title_short | 714. Genotypic Resistance in Acinetobacter as a Predictor for Patient Outcomes |
title_sort | 714. genotypic resistance in acinetobacter as a predictor for patient outcomes |
topic | Abstract |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10677179/ http://dx.doi.org/10.1093/ofid/ofad500.776 |
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