Evidence map›Paper›PMID 34250515›Full record

ArticleThe Lancet. Microbe2021

Blood transcriptional biomarkers of acute viral infection for detection of pre-symptomatic SARS-CoV-2 infection: a nested, case-control diagnostic accuracy study.

Rishi K Gupta, Joshua Rosenheim, Lucy C Bell, Aneesh Chandran, Jose A Guerra-Assuncao, Gabriele Pollara, Matthew Whelan, Jessica Artico, George Joy, Hibba Kurdi and 12 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in The Lancet. Microbe, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
4.2field-weighted citation impact, top 5% 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

38 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.

  1. Pooled it
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  7. medRxiv : the preprint server for health sciences · 2024
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  19. Can T Cells Abort SARS-CoV-2 and Other Viral Infections?International journal of molecular sciences · 2023
    Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

22 authors at 5 institutions in 1 country.

Rishi K GuptaInstitute of Global Health, University College London, London, UK.
Joshua RosenheimDivision of Infection and Immunity, University College London, London, UK.
Lucy C BellDivision of Infection and Immunity, University College London, London, UK.
Aneesh ChandranDivision of Infection and Immunity, University College London, London, UK.
Jose A Guerra-AssuncaoDivision of Infection and Immunity, University College London, London, UK.
Gabriele PollaraDivision of Infection and Immunity, University College London, London, UK.
Matthew WhelanDivision of Infection and Immunity, University College London, London, UK.
Jessica ArticoBarts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
George JoyBarts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
Hibba KurdiBarts Heart Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
Daniel M AltmannDepartment of Immunology and Inflammation, Imperial College London, London, UK.
Rosemary J BoytonLung Division, Royal Brompton & Harefield NHS Foundation Trust, London, UK.
Mala K MainiDivision of Infection and Immunity, University College London, London, UK.
Aine McKnightBlizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Jonathan LambourneDepartment of Infection, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
Teresa Cutino-MoguelDepartment of Virology, St Bartholomew's Hospital, Barts Health NHS Trust, London, UK.
Charlotte ManistyInstitute of Cardiovascular Sciences, University College London, London, UK.
Thomas A TreibelInstitute of Cardiovascular Sciences, University College London, London, UK.
James C MoonInstitute of Cardiovascular Sciences, University College London, London, UK.
Benjamin M ChainDivision of Infection and Immunity, University College London, London, UK.
Mahdad NoursadeghiDivision of Infection and Immunity, University College London, London, UK.
COVIDsortium Investigators
St Bartholomew's Hospital · GBUniversity College London · GBImperial College London · GBRoyal Brompton & Harefield NHS Foundation Trust · GBQueen Mary University of London · GB

Funding

British Heart Foundation AA/18/6/34223British Heart Foundation FS/18/21/33447British Heart Foundation FS/CRTF/21/24128Cancer Research UK 26603Medical Research Council MR/R02622X/1Medical Research Council MR/S019553/1Medical Research Council MR/V036939/1Medical Research Council MR/V036939/1MR/S019553/1Medical Research Council MR/W020610/1MRF_ MRF-044-0001-RG-SWADLWellcome TrustWellcome Trust 207511/Z/17/ZWellcome Trust 214191/Z/18/Z
6 · The paper itself

Abstract

backgroundWe hypothesised that host-response biomarkers of viral infections might contribute to early identification of individuals infected with SARS-CoV-2, which is critical to breaking the chains of transmission. We aimed to evaluate the diagnostic accuracy of existing candidate whole-blood transcriptomic signatures for viral infection to predict positivity of nasopharyngeal SARS-CoV-2 PCR testing.

methodsWe did a nested case-control diagnostic accuracy study among a prospective cohort of health-care workers (aged ≥18 years) at St Bartholomew's Hospital (London, UK) undergoing weekly blood and nasopharyngeal swab sampling for whole-blood RNA sequencing and SARS-CoV-2 PCR testing, when fit to attend work. We identified candidate blood transcriptomic signatures for viral infection through a systematic literature search. We searched MEDLINE for articles published between database inception and Oct 12, 2020, using comprehensive MeSH and keyword terms for "viral infection", "transcriptome", "biomarker", and "blood". We reconstructed signature scores in blood RNA sequencing data and evaluated their diagnostic accuracy for contemporaneous SARS-CoV-2 infection, compared with the gold standard of SARS-CoV-2 PCR testing, by quantifying the area under the receiver operating characteristic curve (AUROC), sensitivities, and specificities at a standardised Z score of at least 2 based on the distribution of signature scores in test-negative controls. We used pairwise DeLong tests compared with the most discriminating signature to identify the subset of best performing biomarkers. We evaluated associations between signature expression, viral load (using PCR cycle thresholds), and symptom status visually and using Spearman rank correlation. The primary outcome was the AUROC for discriminating between samples from participants who tested negative throughout the study (test-negative controls) and samples from participants with PCR-confirmed SARS-CoV-2 infection (test-positive participants) during their first week of PCR positivity.

findingsWe identified 20 candidate blood transcriptomic signatures of viral infection from 18 studies and evaluated their accuracy among 169 blood RNA samples from 96 participants over 24 weeks. Participants were recruited between March 23 and March 31, 2020. 114 samples were from 41 participants with SARS-CoV-2 infection, and 55 samples were from 55 test-negative controls. The median age of participants was 36 years (IQR 27-47) and 69 (72%) of 96 were women. Signatures had little overlap of component genes, but were mostly correlated as components of type I interferon responses. A single blood transcript for

interpretationOur findings support further urgent evaluation and development of blood

fundingBarts Charity, Wellcome Trust, and National Institute of Health Research.

Indexed as

COVID-19AdolescentAdultBiomarkersCase-Control StudiesFemaleHumansMaleMiddle AgedProspective StudiesSARS-CoV-2Sensitivity and SpecificityBiomarkers

Identifiers

PMID34250515
PMCPMC8260104
OpenAlexW3178090203

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.