↓ Skip to main content

Host gene expression classifiers diagnose acute respiratory illness etiology

Overview of attention for article published in Science Translational Medicine, January 2016
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (99th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
42 news outlets
blogs
4 blogs
twitter
116 X users
patent
5 patents
facebook
7 Facebook pages
googleplus
2 Google+ users

Citations

dimensions_citation
200 Dimensions

Readers on

mendeley
215 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Host gene expression classifiers diagnose acute respiratory illness etiology
Published in
Science Translational Medicine, January 2016
DOI 10.1126/scitranslmed.aad6873
Pubmed ID
Authors

Ephraim L Tsalik, Ricardo Henao, Marshall Nichols, Thomas Burke, Emily R Ko, Micah T McClain, Lori L Hudson, Anna Mazur, Debra H Freeman, Tim Veldman, Raymond J Langley, Eugenia B Quackenbush, Seth W Glickman, Charles B Cairns, Anja K Jaehne, Emanuel P Rivers, Ronny M Otero, Aimee K Zaas, Stephen F Kingsmore, Joseph Lucas, Vance G Fowler, Lawrence Carin, Geoffrey S Ginsburg, Christopher W Woods

Abstract

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.

X Demographics

X Demographics

The data shown below were collected from the profiles of 116 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 215 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 2%
United Kingdom 1 <1%
Germany 1 <1%
Taiwan 1 <1%
Unknown 208 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 54 25%
Student > Ph. D. Student 37 17%
Other 23 11%
Student > Master 15 7%
Professor 13 6%
Other 38 18%
Unknown 35 16%
Readers by discipline Count As %
Medicine and Dentistry 52 24%
Agricultural and Biological Sciences 41 19%
Biochemistry, Genetics and Molecular Biology 22 10%
Immunology and Microbiology 19 9%
Computer Science 10 5%
Other 27 13%
Unknown 44 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 411. 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 30 March 2022.
All research outputs
#71,421
of 25,371,288 outputs
Outputs from Science Translational Medicine
#238
of 5,434 outputs
Outputs of similar age
#1,205
of 403,314 outputs
Outputs of similar age from Science Translational Medicine
#3
of 115 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,434 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 86.6. This one has done particularly well, scoring higher than 95% of its peers.
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 403,314 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.