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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Microbiology 2019
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.
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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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