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Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes
We developed a rapid high-throughput PCR test and evaluated highly antibiotic-resistant clinical isolates of Escherichia coli (n = 2,919), Klebsiella pneumoniae (n = 1,974), Proteus mirabilis (n = 1,150), and Pseudomonas aeruginosa (n = 1,484) for several antibiotic resistance genes for comparison w...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6496154/ https://www.ncbi.nlm.nih.gov/pubmed/30917985 http://dx.doi.org/10.1128/AAC.02462-18 |
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author | Walker, G. Terrance Quan, Julia Higgins, Stephen G. Toraskar, Nikhil Chang, Weizhong Saeed, Alexander Sapiro, Vadim Pitzer, Kelsey Whitfield, Natalie Lopansri, Bert K. Motyl, Mary Sahm, Daniel |
author_facet | Walker, G. Terrance Quan, Julia Higgins, Stephen G. Toraskar, Nikhil Chang, Weizhong Saeed, Alexander Sapiro, Vadim Pitzer, Kelsey Whitfield, Natalie Lopansri, Bert K. Motyl, Mary Sahm, Daniel |
author_sort | Walker, G. Terrance |
collection | PubMed |
description | We developed a rapid high-throughput PCR test and evaluated highly antibiotic-resistant clinical isolates of Escherichia coli (n = 2,919), Klebsiella pneumoniae (n = 1,974), Proteus mirabilis (n = 1,150), and Pseudomonas aeruginosa (n = 1,484) for several antibiotic resistance genes for comparison with phenotypic resistance across penicillins, cephalosporins, carbapenems, aminoglycosides, trimethoprim-sulfamethoxazole, fluoroquinolones, and macrolides. The isolates originated from hospitals in North America (34%), Europe (23%), Asia (13%), South America (12%), Africa (7%), or Oceania (1%) or were of unknown origin (9%). We developed statistical methods to predict phenotypic resistance from resistance genes for 49 antibiotic-organism combinations, including gentamicin, tobramycin, ciprofloxacin, levofloxacin, trimethoprim-sulfamethoxazole, ertapenem, imipenem, cefazolin, cefepime, cefotaxime, ceftazidime, ceftriaxone, ampicillin, and aztreonam. Average positive predictive values for genotypic prediction of phenotypic resistance were 91% for E. coli, 93% for K. pneumoniae, 87% for P. mirabilis, and 92% for P. aeruginosa across the various antibiotics for this highly resistant cohort of bacterial isolates. |
format | Online Article Text |
id | pubmed-6496154 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-64961542019-06-03 Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes Walker, G. Terrance Quan, Julia Higgins, Stephen G. Toraskar, Nikhil Chang, Weizhong Saeed, Alexander Sapiro, Vadim Pitzer, Kelsey Whitfield, Natalie Lopansri, Bert K. Motyl, Mary Sahm, Daniel Antimicrob Agents Chemother Mechanisms of Resistance We developed a rapid high-throughput PCR test and evaluated highly antibiotic-resistant clinical isolates of Escherichia coli (n = 2,919), Klebsiella pneumoniae (n = 1,974), Proteus mirabilis (n = 1,150), and Pseudomonas aeruginosa (n = 1,484) for several antibiotic resistance genes for comparison with phenotypic resistance across penicillins, cephalosporins, carbapenems, aminoglycosides, trimethoprim-sulfamethoxazole, fluoroquinolones, and macrolides. The isolates originated from hospitals in North America (34%), Europe (23%), Asia (13%), South America (12%), Africa (7%), or Oceania (1%) or were of unknown origin (9%). We developed statistical methods to predict phenotypic resistance from resistance genes for 49 antibiotic-organism combinations, including gentamicin, tobramycin, ciprofloxacin, levofloxacin, trimethoprim-sulfamethoxazole, ertapenem, imipenem, cefazolin, cefepime, cefotaxime, ceftazidime, ceftriaxone, ampicillin, and aztreonam. Average positive predictive values for genotypic prediction of phenotypic resistance were 91% for E. coli, 93% for K. pneumoniae, 87% for P. mirabilis, and 92% for P. aeruginosa across the various antibiotics for this highly resistant cohort of bacterial isolates. American Society for Microbiology 2019-03-27 /pmc/articles/PMC6496154/ /pubmed/30917985 http://dx.doi.org/10.1128/AAC.02462-18 Text en Copyright © 2019 Walker et al. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Mechanisms of Resistance Walker, G. Terrance Quan, Julia Higgins, Stephen G. Toraskar, Nikhil Chang, Weizhong Saeed, Alexander Sapiro, Vadim Pitzer, Kelsey Whitfield, Natalie Lopansri, Bert K. Motyl, Mary Sahm, Daniel Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes |
title | Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes |
title_full | Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes |
title_fullStr | Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes |
title_full_unstemmed | Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes |
title_short | Predicting Antibiotic Resistance in Gram-Negative Bacilli from Resistance Genes |
title_sort | predicting antibiotic resistance in gram-negative bacilli from resistance genes |
topic | Mechanisms of Resistance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6496154/ https://www.ncbi.nlm.nih.gov/pubmed/30917985 http://dx.doi.org/10.1128/AAC.02462-18 |
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