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Identification of sepsis subtypes in critically ill adults using gene expression profiling

Overview of attention for article published in Critical Care, October 2012
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  • Good Attention Score compared to outputs of the same age (75th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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3 X users
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2 patents

Citations

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52 Dimensions

Readers on

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67 Mendeley
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1 CiteULike
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Title
Identification of sepsis subtypes in critically ill adults using gene expression profiling
Published in
Critical Care, October 2012
DOI 10.1186/cc11667
Pubmed ID
Authors

David M Maslove, Benjamin M Tang, Anthony S McLean

Abstract

ABSTRACT: INTRODUCTION: Sepsis is a syndromic illness that has traditionally been defined by a set of broad, highly sensitive clinical parameters. As a result, numerous distinct pathophysiologic states may meet diagnostic criteria for sepsis, leading to syndrome heterogeneity. The existence of biologically distinct sepsis subtypes may in part explain the lack of actionable evidence from clinical trials of sepsis therapies. We used microarray-based gene expression data from adult patients with sepsis in order to identify molecularly distinct sepsis subtypes. METHODS: We used partitioning around medoids (PAM) and hierarchical clustering of gene expression profiles from neutrophils taken from a cohort of septic patients in order to identify distinct subtypes. Using the medoids learned from this cohort, we then clustered a second independent cohort of septic patients, and used the resulting class labels to evaluate differences in clinical parameters, as well as the expression of relevant pharmacogenes. RESULTS: We identified two sepsis subtypes based on gene expression patterns. Subtype 1 was characterized by increased expression of genes involved in inflammatory and Toll receptor mediated signaling pathways, as well as a higher prevalence of severe sepsis. There were differences between subtypes in the expression of pharmacogenes related to hydrocortisone, vasopressin, norepinephrine, and drotrecogin alpha. CONCLUSIONS: Sepsis subtypes can be identified based on different gene expression patterns. These patterns may generate hypotheses about the underlying pathophysiology of sepsis and suggest new ways of classifying septic patients both in clinical practice, and in the design of clinical trials.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 3%
Germany 1 1%
Indonesia 1 1%
Unknown 63 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 22%
Student > Master 10 15%
Researcher 9 13%
Student > Bachelor 7 10%
Other 5 7%
Other 8 12%
Unknown 13 19%
Readers by discipline Count As %
Medicine and Dentistry 21 31%
Agricultural and Biological Sciences 7 10%
Biochemistry, Genetics and Molecular Biology 6 9%
Immunology and Microbiology 3 4%
Engineering 3 4%
Other 11 16%
Unknown 16 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 December 2022.
All research outputs
#6,443,738
of 25,374,917 outputs
Outputs from Critical Care
#3,684
of 6,554 outputs
Outputs of similar age
#47,287
of 191,553 outputs
Outputs of similar age from Critical Care
#34
of 114 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 6,554 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.8. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 191,553 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 114 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.