Evidence map›Paper›PMID 34462743›Full record

ArticlebioRxiv : the preprint server for biology2021

Gene Expression Risk Scores for COVID-19 Illness Severity.

Derick R Peterson, Andrea M Baran, Soumyaroop Bhattacharya, Angela R Branche, Daniel P Croft, Anthony M Corbett, Edward E Walsh, Ann R Falsey, Thomas J Mariani

Open access · greenAbstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors at 1 institution in 1 country.

Derick R PetersonDepartment of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.
Andrea M BaranDepartment of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.
Soumyaroop BhattacharyaDivision of Neonatology and Pediatric Molecular and Personalized Medicine Program, Department of Pediatrics, University of Rochester, Rochester, NY, USA.
Angela R BrancheDivision of Infectious Diseases, Department of Medicine, University of Rochester, Rochester, NY, USA.
Daniel P CroftDivision of Pulmonary and Critical Care, Department of Medicine, University of Rochester, Rochester, NY, USA.
Anthony M CorbettDepartment of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.
Edward E WalshDivision of Infectious Diseases, Department of Medicine, University of Rochester, Rochester, NY, USA.
Ann R FalseyDivision of Infectious Diseases, Department of Medicine, University of Rochester, Rochester, NY, USA.
Thomas J MarianiDivision of Neonatology and Pediatric Molecular and Personalized Medicine Program, Department of Pediatrics, University of Rochester, Rochester, NY, USA.
Rochester General Hospital · US

Funding

VISUAL INDICES OF NEUROTOXICITYP30ES001247 · NIEHS · UNIVERSITY OF ROCHESTER · PI Martha Susiarjo · 1985 to 2026
$42.8M
Transcriptional Profiling to Discriminate Bacterial and Non-bacterial Respiratory IllnessesR01AI137364 · NIAID · UNIVERSITY OF ROCHESTER · PI FALSEY, ANN R, MARIANI, THOMAS J · 2019 to 2023
$3.8M
Effect of Air Pollution on the Immune Response to Respiratory Viral Infection in AdultsK23ES032459 · NIEHS · UNIVERSITY OF ROCHESTER · PI CROFT, DANIEL PATRICK · 2021 to 2025
$1.2M
NIAID NIH HHS R01 AI137364NIEHS NIH HHS K23 ES032459NIEHS NIH HHS P30 ES001247
6 · The paper itself

Abstract

backgroundThe correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.

methodsWe assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID.

resultsGene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.

conclusionThese data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.

Identifiers

PMID34462743
PMCPMC8404885
OpenAlexW3196251096

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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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.