Evidence map›Paper›PMID 34088944›Full record

ArticleScientific reports2021

Analytic comparison between three high-throughput commercial SARS-CoV-2 antibody assays reveals minor discrepancies in a high-incidence population.

Gheyath K Nasrallah, Soha R Dargham, Farah Shurrab, Duaa W Al-Sadeq, Hadeel Al-Jighefee, Hiam Chemaitelly, Zaina Al Kanaani, Abdullatif Al Khal, Einas Al Kuwari, Peter Coyle and 14 more

Open access · goldAbstract readComparative Study
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.6field-weighted citation impact, top 14% 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

9 citing papers in PubMed, 25 citations in OpenAlex.

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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

24 authors at 5 institutions in 1 country.

Gheyath K NasrallahBiomedical Research Center, QU Health, Qatar University, 2713, Doha, Qatar. gheyath.nasrallah@qu.edu.qa.
Soha R DarghamInfectious Disease Epidemiology Group, Weill Cornell Medicine - Qatar, Qatar Foundation - Education City, Cornell University, P.O. Box 24144, Doha, Qatar.
Farah ShurrabBiomedical Research Center, QU Health, Qatar University, 2713, Doha, Qatar.
Duaa W Al-SadeqBiomedical Research Center, QU Health, Qatar University, 2713, Doha, Qatar.
Hadeel Al-JighefeeBiomedical Research Center, QU Health, Qatar University, 2713, Doha, Qatar.
Hiam ChemaitellyInfectious Disease Epidemiology Group, Weill Cornell Medicine - Qatar, Qatar Foundation - Education City, Cornell University, P.O. Box 24144, Doha, Qatar.
Zaina Al KanaaniHamad Medical Corporation, Doha, Qatar.
Abdullatif Al KhalHamad Medical Corporation, Doha, Qatar.
Einas Al KuwariHamad Medical Corporation, Doha, Qatar.
Peter CoyleHamad Medical Corporation, Doha, Qatar.
Andrew JeremijenkoHamad Medical Corporation, Doha, Qatar.
Anvar Hassan KaleeckalHamad Medical Corporation, Doha, Qatar.
Ali Nizar LatifHamad Medical Corporation, Doha, Qatar.
Riyazuddin Mohammad ShaikHamad Medical Corporation, Doha, Qatar.
Hanan F Abdul RahimCollege of Health Sciences, QU Health, Qatar University, Doha, Qatar.
Hadi M YassineBiomedical Research Center, QU Health, Qatar University, 2713, Doha, Qatar.
Mohamed G Al KuwariPrimary Health Care Corporation, Doha, Qatar.
Hamda QotbaPrimary Health Care Corporation, Doha, Qatar.
Hamad Eid Al RomaihiMinistry of Public Health, Doha, Qatar.
Patrick TangDepartment of Pathology, Sidra Medicine, Doha, Qatar.
Roberto BertolliniMinistry of Public Health, Doha, Qatar.
Mohamed H Al-ThaniMinistry of Public Health, Doha, Qatar.
Asmaa A AlthaniBiomedical Research Center, QU Health, Qatar University, 2713, Doha, Qatar.
Laith J Abu-RaddadInfectious Disease Epidemiology Group, Weill Cornell Medicine - Qatar, Qatar Foundation - Education City, Cornell University, P.O. Box 24144, Doha, Qatar. lja2002@qatar-med.cornell.edu.
Hamad Medical Corporation · QAQatar University · QAMinistry of Public Health · QAWeill Cornell Medical College in Qatar · QAPrimary Health Care · QA

Funding

Qatar National Research Fund Grant number RRC-2-032
6 · The paper itself

Abstract

Performance of three automated commercial serological IgG-based assays was investigated for assessing SARS-CoV-2 "ever" (past or current) infection in a population-based sample in a high exposure setting. PCR and serological testing was performed on 394 individuals. SARS-CoV-2-IgG seroprevalence was 42.9% (95% CI 38.1-47.8%), 40.6% (95% CI 35.9-45.5%), and 42.4% (95% CI 37.6-47.3%) using the CL-900i, VidasIII, and Elecsys assays, respectively. Between the three assays, overall, positive, and negative percent agreements ranged between 93.2-95.7%, 89.3-92.8%, and 93.8-97.8%, respectively; Cohen's kappa statistic ranged from 0.86 to 0.91; and 35 specimens (8.9%) showed discordant results. Among all individuals, 12.5% (95% CI 9.6-16.1%) had current infection, as assessed by PCR. Of these, only 34.7% (95% CI 22.9-48.7%) were seropositive by at least one assay. A total of 216 individuals (54.8%; 95% CI 49.9-59.7%) had evidence of ever infection using antibody testing and/or PCR during or prior to this study. Of these, only 78.2%, 74.1%, and 77.3% were seropositive in the CL-900i, VidasIII, and Elecsys assays, respectively. All three assays had comparable performance and excellent agreement, but missed at least 20% of individuals with past or current infection. Commercial antibody assays can substantially underestimate ever infection, more so when infection rates are high.

Indexed as

Antibodies, ViralCOVID-19COVID-19 Serological TestingHumansImmunoglobulin GIncidenceSARS-CoV-2Sensitivity and SpecificityAntibodies, ViralImmunoglobulin G

Identifiers

PMID34088944
PMCPMC8178338
OpenAlexW3164321442

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.