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Biochemical Phenotypes to Discriminate Microbial Subpopulations and Improve Outbreak Detection

BACKGROUND: Clinical microbiology laboratories worldwide constitute an invaluable resource for monitoring emerging threats and the spread of antimicrobial resistance. We studied the growing number of biochemical tests routinely performed on clinical isolates to explore their value as epidemiological...

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Autores principales: Galar, Alicia, Kulldorff, Martin, Rudnick, Wallis, O'Brien, Thomas F., Stelling, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3877295/
https://www.ncbi.nlm.nih.gov/pubmed/24391936
http://dx.doi.org/10.1371/journal.pone.0084313
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author Galar, Alicia
Kulldorff, Martin
Rudnick, Wallis
O'Brien, Thomas F.
Stelling, John
author_facet Galar, Alicia
Kulldorff, Martin
Rudnick, Wallis
O'Brien, Thomas F.
Stelling, John
author_sort Galar, Alicia
collection PubMed
description BACKGROUND: Clinical microbiology laboratories worldwide constitute an invaluable resource for monitoring emerging threats and the spread of antimicrobial resistance. We studied the growing number of biochemical tests routinely performed on clinical isolates to explore their value as epidemiological markers. METHODOLOGY/PRINCIPAL FINDINGS: Microbiology laboratory results from January 2009 through December 2011 from a 793-bed hospital stored in WHONET were examined. Variables included patient location, collection date, organism, and 47 biochemical and 17 antimicrobial susceptibility test results reported by Vitek 2. To identify biochemical tests that were particularly valuable (stable with repeat testing, but good variability across the species) or problematic (inconsistent results with repeat testing), three types of variance analyses were performed on isolates of K. pneumonia: descriptive analysis of discordant biochemical results in same-day isolates, an average within-patient variance index, and generalized linear mixed model variance component analysis. Results: 4,200 isolates of K. pneumoniae were identified from 2,485 patients, 32% of whom had multiple isolates. The first two variance analyses highlighted SUCT, TyrA, GlyA, and GGT as “nuisance” biochemicals for which discordant within-patient test results impacted a high proportion of patient results, while dTAG had relatively good within-patient stability with good heterogeneity across the species. Variance component analyses confirmed the relative stability of dTAG, and identified additional biochemicals such as PHOS with a large between patient to within patient variance ratio. A reduced subset of biochemicals improved the robustness of strain definition for carbapenem-resistant K. pneumoniae. Surveillance analyses suggest that the reduced biochemical profile could improve the timeliness and specificity of outbreak detection algorithms. CONCLUSIONS: The statistical approaches explored can improve the robust recognition of microbial subpopulations with routinely available biochemical test results, of value in the timely detection of outbreak clones and evolutionarily important genetic events.
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spelling pubmed-38772952014-01-03 Biochemical Phenotypes to Discriminate Microbial Subpopulations and Improve Outbreak Detection Galar, Alicia Kulldorff, Martin Rudnick, Wallis O'Brien, Thomas F. Stelling, John PLoS One Research Article BACKGROUND: Clinical microbiology laboratories worldwide constitute an invaluable resource for monitoring emerging threats and the spread of antimicrobial resistance. We studied the growing number of biochemical tests routinely performed on clinical isolates to explore their value as epidemiological markers. METHODOLOGY/PRINCIPAL FINDINGS: Microbiology laboratory results from January 2009 through December 2011 from a 793-bed hospital stored in WHONET were examined. Variables included patient location, collection date, organism, and 47 biochemical and 17 antimicrobial susceptibility test results reported by Vitek 2. To identify biochemical tests that were particularly valuable (stable with repeat testing, but good variability across the species) or problematic (inconsistent results with repeat testing), three types of variance analyses were performed on isolates of K. pneumonia: descriptive analysis of discordant biochemical results in same-day isolates, an average within-patient variance index, and generalized linear mixed model variance component analysis. Results: 4,200 isolates of K. pneumoniae were identified from 2,485 patients, 32% of whom had multiple isolates. The first two variance analyses highlighted SUCT, TyrA, GlyA, and GGT as “nuisance” biochemicals for which discordant within-patient test results impacted a high proportion of patient results, while dTAG had relatively good within-patient stability with good heterogeneity across the species. Variance component analyses confirmed the relative stability of dTAG, and identified additional biochemicals such as PHOS with a large between patient to within patient variance ratio. A reduced subset of biochemicals improved the robustness of strain definition for carbapenem-resistant K. pneumoniae. Surveillance analyses suggest that the reduced biochemical profile could improve the timeliness and specificity of outbreak detection algorithms. CONCLUSIONS: The statistical approaches explored can improve the robust recognition of microbial subpopulations with routinely available biochemical test results, of value in the timely detection of outbreak clones and evolutionarily important genetic events. Public Library of Science 2013-12-31 /pmc/articles/PMC3877295/ /pubmed/24391936 http://dx.doi.org/10.1371/journal.pone.0084313 Text en © 2013 Galar et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Galar, Alicia
Kulldorff, Martin
Rudnick, Wallis
O'Brien, Thomas F.
Stelling, John
Biochemical Phenotypes to Discriminate Microbial Subpopulations and Improve Outbreak Detection
title Biochemical Phenotypes to Discriminate Microbial Subpopulations and Improve Outbreak Detection
title_full Biochemical Phenotypes to Discriminate Microbial Subpopulations and Improve Outbreak Detection
title_fullStr Biochemical Phenotypes to Discriminate Microbial Subpopulations and Improve Outbreak Detection
title_full_unstemmed Biochemical Phenotypes to Discriminate Microbial Subpopulations and Improve Outbreak Detection
title_short Biochemical Phenotypes to Discriminate Microbial Subpopulations and Improve Outbreak Detection
title_sort biochemical phenotypes to discriminate microbial subpopulations and improve outbreak detection
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3877295/
https://www.ncbi.nlm.nih.gov/pubmed/24391936
http://dx.doi.org/10.1371/journal.pone.0084313
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