Evidence map›Paper›PMID 32979932›Full record

ArticleThe Lancet. Infectious diseases2021

A blood-based host gene expression assay for early detection of respiratory viral infection: an index-cluster prospective cohort study.

Micah T McClain, Florica J Constantine, Bradly P Nicholson, Marshall Nichols, Thomas W Burke, Ricardo Henao, Daphne C Jones, Lori L Hudson, L Brett Jaggers, Timothy Veldman and 6 more

Open access · bronzeAbstract read
In one paragraph

Article in The Lancet. Infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
4.8field-weighted citation impact, top 4% of its field
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

30 citing papers in PubMed, 47 citations in OpenAlex.

  1. Article
  2. Article
  3. GIDISdb: a gene expression database for exploring human immune responses in infectious diseases.Database : the journal of biological databases and curation · 2026
    Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. medRxiv : the preprint server for health sciences · 2024
    Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

16 authors at 3 institutions in 1 country.

Micah T McClainCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA; Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA; Durham VA Medical Center, Durham, NC, USA. Electronic address: micah.mcclain@duke.edu.
Florica J ConstantineCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
Bradly P NicholsonInstitute for Medical Research, Durham, NC, USA.
Marshall NicholsCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
Thomas W BurkeCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
Ricardo HenaoCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
Daphne C JonesDurham VA Medical Center, Durham, NC, USA.
Lori L HudsonCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
L Brett JaggersDivision of Infectious Diseases, Duke University Medical Center, Durham, NC, USA.
Timothy VeldmanCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
Anna MazurCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
Lawrence P ParkDivision of Infectious Diseases, Duke University Medical Center, Durham, NC, USA; Durham VA Medical Center, Durham, NC, USA.
Sunil SuchindranCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
Ephraim L TsalikCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA; Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA; Durham VA Medical Center, Durham, NC, USA.
Geoffrey S GinsburgCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA.
Christopher W WoodsCenter for Applied Genomics and Precision Medicine, Duke University Medical Center, Durham, NC, USA; Division of Infectious Diseases, Duke University Medical Center, Durham, NC, USA; Durham VA Medical Center, Durham, NC, USA.
Duke Medical Center · USDurham VA Medical Center · USInstitute for Medical Research · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly and accurate identification of individuals with viral infections is crucial for clinical management and public health interventions. We aimed to assess the ability of transcriptomic biomarkers to identify naturally acquired respiratory viral infection before typical symptoms are present.

methodsIn this index-cluster study, we prospectively recruited a cohort of undergraduate students (aged 18-25 years) at Duke University (Durham, NC, USA) over a period of 5 academic years. To identify index cases, we monitored students for the entire academic year, for the presence and severity of eight symptoms of respiratory tract infection using a daily web-based survey, with symptoms rated on a scale of 0-4. Index cases were defined as individuals who reported a 6-point increase in cumulative daily symptom score. Suspected index cases were visited by study staff to confirm the presence of reported symptoms of illness and to collect biospecimen samples. We then identified clusters of close contacts of index cases (ie, individuals who lived in close proximity to index cases, close friends, and partners) who were presumed to be at increased risk of developing symptomatic respiratory tract infection while under observation. We monitored each close contact for 5 days for symptoms and viral shedding and measured transcriptomic responses at each timepoint each day using a blood-based 36-gene RT-PCR assay.

findingsBetween Sept 1, 2009, and April 10, 2015, we enrolled 1465 participants. Of 264 index cases with respiratory tract infection symptoms, 150 (57%) had a viral cause confirmed by RT-PCR. Of their 555 close contacts, 106 (19%) developed symptomatic respiratory tract infection with a proven viral cause during the observation window, of whom 60 (57%) had the same virus as their associated index case. Nine viruses were detected in total. The transcriptomic assay accurately predicted viral infection at the time of maximum symptom severity (mean area under the receiver operating characteristic curve [AUROC] 0·94 [95% CI 0·92-0·96]), as well as at 1 day (0·87 [95% CI 0·84-0·90]), 2 days (0·85 [0·82-0·88]), and 3 days (0·74 [0·71-0·77]) before peak illness, when symptoms were minimal or absent and 22 (62%) of 35 individuals, 25 (69%) of 36 individuals, and 24 (82%) of 29 individuals, respectively, had no detectable viral shedding.

interpretationTranscriptional biomarkers accurately predict and diagnose infection across diverse viral causes and stages of disease and thus might prove useful for guiding the administration of early effective therapy, quarantine decisions, and other clinical and public health interventions in the setting of endemic and pandemic infectious diseases.

fundingUS Defense Advanced Research Projects Agency.

Indexed as

AdolescentAdultBiomarkersFemaleHumansLogistic ModelsMaleProspective StudiesRespiratory Tract InfectionsReverse Transcriptase Polymerase Chain ReactionRNA, ViralTranscription FactorsVirus DiseasesYoung AdultBiomarkersRNA, ViralTranscription Factors

Identifiers

PMID32979932
PMCPMC7515566
OpenAlexW3087775772

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.