Evidence map›Paper›PMID 33043818›Full record

ArticleEmerging microbes & infections2020

Validation and clinical evaluation of a SARS-CoV-2 surrogate virus neutralisation test (sVNT).

Benjamin Meyer, Johan Reimerink, Giulia Torriani, Fion Brouwer, Gert-Jan Godeke, Sabine Yerly, Marieke Hoogerwerf, Nicolas Vuilleumier, Laurent Kaiser, Isabella Eckerle and 1 more

Open access · goldAbstract readValidation Study
In one paragraph

Article in Emerging microbes & infections, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 107 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
107citing papers in PubMed, 1 pooled it
3.3field-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

107 citing papers in PubMed, 1 synthesis or guideline pooled it, 149 citations in OpenAlex.

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47 more citing papers are in PubMed but not listed here.

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

11 authors at 4 institutions in 3 countries.

Benjamin MeyerCentre for Vaccinology, Department of Pathology and Immunology, University of Geneva, Geneva, Switzerland.ORCID https://orcid.org/0000-0003-0601-3550
Johan ReimerinkCentre for Infectious Disease Control, WHO COVID-19 reference laboratory, RIVM, Bilthoven, Netherlands.ORCID https://orcid.org/0000-0002-0785-8996
Giulia TorrianiDepartment of Microbiology and Molecular Medicine, University of Geneva, Geneva, Switzerland.
Fion BrouwerCentre for Infectious Disease Control, WHO COVID-19 reference laboratory, RIVM, Bilthoven, Netherlands.
Gert-Jan GodekeCentre for Infectious Disease Control, WHO COVID-19 reference laboratory, RIVM, Bilthoven, Netherlands.
Sabine YerlyLaboratory of Virology, Geneva University Hospitals, Geneva, Switzerland.
Marieke HoogerwerfCentre for Infectious Disease Control, WHO COVID-19 reference laboratory, RIVM, Bilthoven, Netherlands.
Nicolas VuilleumierDivision of Laboratory Medicine, Department of Diagnostics, Geneva University Hospitals and Geneva University, Geneva, Switzerland.
Laurent KaiserLaboratory of Virology, Geneva University Hospitals, Geneva, Switzerland.
Isabella EckerleDepartment of Microbiology and Molecular Medicine, University of Geneva, Geneva, Switzerland.
Chantal ReuskenCentre for Infectious Disease Control, WHO COVID-19 reference laboratory, RIVM, Bilthoven, Netherlands.ORCID https://orcid.org/0000-0003-4605-9174
National Research Institute for Veterinary Virology and Microbiology of Russia · RUUniversity of Geneva · CHUniversity Hospital of Geneva · CHGeneva College · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

To understand SARS-CoV-2 immunity after natural infection or vaccination, functional assays such as virus neutralising assays are needed. So far, assays to detect SARS-CoV-2 neutralising antibodies rely on cell-culture based infection assays either using wild type SARS-CoV-2 or pseudotyped viruses. Such assays are labour-intensive, require appropriate biosafety facilities and are difficult to standardize. Recently, a new surrogate virus neutralisation test (sVNT) was described that uses the principle of an ELISA to measure the neutralisation capacity of anti-SARS-CoV-2 antibodies directed against the receptor binding domain. Here, we performed an independent evaluation of the robustness, specificity and sensitivity on an extensive panel of sera from 269 PCR-confirmed COVID-19 cases and 259 unmatched samples collected before 2020 and compared it to cell-based neutralisation assays. We found a high specificity of 99.2 (95%CI: 96.9-99.9) and overall sensitivity of 80.3 (95%CI: 74.9-84.8) for the sVNT. Clinical sensitivity increased between early (<14 days post symptom onset or post diagnosis, dpos/dpd) and late sera (>14 dpos/dpd) from 75.0 (64.7-83.2) to 83.1 (76.5-88.1). Also, higher severity was associated with an increase in clinical sensitivity. Upon comparison with cell-based neutralisation assays we determined an analytical sensitivity of 74.3 (56.4-86.9) and 98.2 (89.4-99.9) for titres ≥10 to <40 and ≥40 to <160, respectively. Only samples with a titre ≥160 were always positive in the sVNT. In conclusion, the sVNT can be used as an additional assay to determine the immune status of COVID-19 infected of vaccinated individuals but its value needs to be assessed for each specific context.

Indexed as

AdultAgedAntibodies, NeutralizingAntibodies, ViralBetacoronavirusCoronavirus InfectionsCOVID-19Enzyme-Linked Immunosorbent AssayFemaleHumansMaleMiddle AgedNeutralization TestsPandemicsPneumonia, ViralSARS-CoV-2Antibodies, NeutralizingAntibodies, Viralcell-based virus neutralisation assayneutralising antibodiespseudovirus neutralisation assaySARS-CoV-2surrogate virus neutralisation assay

Identifiers

PMID33043818
PMCPMC7605318
OpenAlexW3092580096

What OpenQuestion holds

Textmetadata
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.