Evidence map›Paper›PMID 24391936›Full record

ArticlePloS one2013

Biochemical phenotypes to discriminate microbial subpopulations and improve outbreak detection.

Alicia Galar, Martin Kulldorff, Wallis Rudnick, Thomas F O'Brien, John Stelling

Abstract read
In one paragraph

Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Alicia GalarWHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
Martin KulldorffDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts, United States of America.
Wallis RudnickDepartment of Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada.
Thomas F O'BrienWHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
John StellingWHO Collaborating Centre for Surveillance of Antimicrobial Resistance, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.

Funding

WHONET: Tools for a Global Microbial Sensor NetworkR01GM103525 · NIGMS · BRIGHAM AND WOMEN'S HOSPITAL · PI STELLING, JOHN · 2012 to 2016
$1.9M
NIGMS NIH HHS R01 GM103525NIGMS NIH HHS R01GM103525
6 · The paper itself

Abstract

backgroundClinical 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

findingsMicrobiology 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.

results4,200 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.

conclusionsThe 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.

Indexed as

Disease OutbreaksPhenotypeAnalysis of VarianceBiomarkersCluster AnalysisCommunicable Diseases, EmergingDrug Resistance, MicrobialHumansKlebsiella InfectionsKlebsiella pneumoniaeBiomarkers

Identifiers

PMID24391936
PMCPMC3877295

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.