Evidence map›Paper›PMID 33609075›Full record

SynthesisInfluenza and other respiratory viruses2021

Diagnostic performance of COVID-19 serological assays during early infection: A systematic review and meta-analysis of 11 516 samples.

John J Y Zhang, Keng Siang Lee, Chee Wui Ong, Mae Yee Chan, Li Wei Ang, Yee Sin Leo, Mark I-Cheng Chen, David Chien Boon Lye, Barnaby Edward Young

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in Influenza and other respiratory viruses, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 4 pooled it
–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

16 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  3. Auxiliary Screening COVID-19 by Serology.Frontiers in public health · 2022
    Pooled it
  4. Pooled it
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  14. Diagnostic Properties of Three SARS-CoV-2 Antibody Tests.Diagnostics (Basel, Switzerland) · 2021
    Article
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  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

John J Y ZhangYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-8567-3807
Keng Siang LeeBristol Medical School, Faculty of Health Sciences, University of Bristol, Bristol, UK.ORCID 0000-0003-2308-0579
Chee Wui OngYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-0470-9725
Mae Yee ChanYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID 0000-0002-1537-2945
Li Wei AngNational Centre for Infectious Diseases, Singapore, Singapore.ORCID 0000-0002-0036-255X
Yee Sin LeoYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-4978-5825
Mark I-Cheng ChenNational Centre for Infectious Diseases, Singapore, Singapore.ORCID 0000-0001-9369-5830
David Chien Boon LyeYong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.ORCID 0000-0003-0324-0205
Barnaby Edward YoungNational Centre for Infectious Diseases, Singapore, Singapore.ORCID 0000-0003-1010-2230

Funding

National Medical Research Council
6 · The paper itself

Abstract

objectiveThe use of coronavirus disease 2019 (COVID-19) serological testing to diagnose acute infection or determine population seroprevalence relies on understanding assay accuracy during early infection. We aimed to evaluate the diagnostic performance of serological testing in COVID-19 by providing summary sensitivity and specificity estimates with time from symptom onset.

methodsA systematic search of Ovid MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL) and PubMed was performed up to May 13, 2020. All English language, original peer-reviewed publications reporting the diagnostic performance of serological testing vis-à-vis virologically confirmed SARS-CoV-2 infection were included.

resultsOur search yielded 599 unique publications. A total of 39 publications reporting 11 516 samples from 8872 human participants met eligibility criteria for inclusion in our study. Pooled percentages of IgM and IgG seroconversion by Day 7, 14, 21, 28 and after Day 28 were 37.5%, 73.3%, 81.3%, 72.3% and 73.3%, and 35.4%, 80.6%, 93.3%, 84.4% and 98.9%, respectively. By Day 21, summary estimate of IgM sensitivity was 0.872 (95% CI: 0.784-0.928) and specificity 0.973 (95% CI: 0.938-0.988), while IgG sensitivity was 0.913 (95% CI: 0.823-0.959) and specificity 0.960 (95% CI: 0.919-0.980). On meta-regression, IgM and IgG test accuracy was significantly higher at Day 14 using enzyme-linked immunosorbent assay (ELISA) compared to other methods.

conclusionsSerological assays offer imperfect sensitivity for the diagnosis of acute SARS-CoV-2 infection. Estimates of population seroprevalence during or shortly after an outbreak will need to adjust for the delay between infection, symptom onset and seroconversion.

Indexed as

COVID-19 Serological TestingAntibodies, ViralCOVID-19Evaluation Studies as TopicHumansImmunoglobulin GImmunoglobulin MSARS-CoV-2Sensitivity and SpecificitySeroconversionAntibodies, ViralImmunoglobulin GImmunoglobulin MantibodyCLIACOVID-19diagnosisELISAICAimmunoglobulinserology

Identifiers

PMID33609075
PMCPMC8013346

What OpenQuestion holds

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LicenceCC BY
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.