Evidence map›Paper›PMID 32611558›Full record

SynthesisBMJ (Clinical research ed.)2020

Diagnostic accuracy of serological tests for covid-19: systematic review and meta-analysis.

Mayara Lisboa Bastos, Gamuchirai Tavaziva, Syed Kunal Abidi, Jonathon R Campbell, Louis-Patrick Haraoui, James C Johnston, Zhiyi Lan, Stephanie Law, Emily MacLean, Anete Trajman and 3 more

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BMJ (Clinical research ed.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 473 papers, 16 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
473citing papers in PubMed, 16 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

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

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  11. A systematic review and meta-analysis of the accuracy of SARS-COV-2 IGM and IGG tests in individuals with COVID-19.Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2022
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  15. COVID-19 Prevalence among Healthcare Workers. A Systematic Review and Meta-Analysis.International journal of environmental research and public health · 2021
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413 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Mayara Lisboa BastosRespiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
Gamuchirai TavazivaRespiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
Syed Kunal AbidiRespiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
Jonathon R CampbellRespiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
Louis-Patrick HaraouiDepartment of Microbiology and Infectious Diseases, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, Québec, Canada.
James C JohnstonUniversity of British Columbia, Vancouver, Canada.
Zhiyi LanRespiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
Stephanie LawDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA.
Emily MacLeanDepartments of Epidemiology, Biostatistics and Occupational Health, and Medicine, McGill University, Montreal, Canada.
Anete TrajmanRespiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
Dick MenziesRespiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
Andrea BenedettiRespiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada.
Faiz Ahmad KhanRespiratory Epidemiology and Clinical Research Unit, Centre for Outcomes Research and Evaluation, Research Institute of the McGill University Health Centre, Montreal, Canada faiz.ahmadkhan@mcgill.ca.ORCID 0000-0003-0473-8734

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo determine the diagnostic accuracy of serological tests for coronavirus disease-2019 (covid-19).

designSystematic review and meta-analysis. DATA SOURCES: Medline, bioRxiv, and medRxiv from 1 January to 30 April 2020, using subject headings or subheadings combined with text words for the concepts of covid-19 and serological tests for covid-19. ELIGIBILITY CRITERIA AND DATA ANALYSIS: Eligible studies measured sensitivity or specificity, or both of a covid-19 serological test compared with a reference standard of viral culture or reverse transcriptase polymerase chain reaction. Studies were excluded with fewer than five participants or samples. Risk of bias was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using random effects bivariate meta-analyses.

main outcome measuresThe primary outcome was overall sensitivity and specificity, stratified by method of serological testing (enzyme linked immunosorbent assays (ELISAs), lateral flow immunoassays (LFIAs), or chemiluminescent immunoassays (CLIAs)) and immunoglobulin class (IgG, IgM, or both). Secondary outcomes were stratum specific sensitivity and specificity within subgroups defined by study or participant characteristics, including time since symptom onset.

results5016 references were identified and 40 studies included. 49 risk of bias assessments were carried out (one for each population and method evaluated). High risk of patient selection bias was found in 98% (48/49) of assessments and high or unclear risk of bias from performance or interpretation of the serological test in 73% (36/49). Only 10% (4/40) of studies included outpatients. Only two studies evaluated tests at the point of care. For each method of testing, pooled sensitivity and specificity were not associated with the immunoglobulin class measured. The pooled sensitivity of ELISAs measuring IgG or IgM was 84.3% (95% confidence interval 75.6% to 90.9%), of LFIAs was 66.0% (49.3% to 79.3%), and of CLIAs was 97.8% (46.2% to 100%). In all analyses, pooled sensitivity was lower for LFIAs, the potential point-of-care method. Pooled specificities ranged from 96.6% to 99.7%. Of the samples used for estimating specificity, 83% (10 465/12 547) were from populations tested before the epidemic or not suspected of having covid-19. Among LFIAs, pooled sensitivity of commercial kits (65.0%, 49.0% to 78.2%) was lower than that of non-commercial tests (88.2%, 83.6% to 91.3%). Heterogeneity was seen in all analyses. Sensitivity was higher at least three weeks after symptom onset (ranging from 69.9% to 98.9%) compared with within the first week (from 13.4% to 50.3%).

conclusionHigher quality clinical studies assessing the diagnostic accuracy of serological tests for covid-19 are urgently needed. Currently, available evidence does not support the continued use of existing point-of-care serological tests. STUDY REGISTRATION: PROSPERO CRD42020179452.

Indexed as

Antibodies, ViralBetacoronavirusClinical Laboratory TechniquesCoronavirus InfectionsCOVID-19COVID-19 TestingEnzyme-Linked Immunosorbent AssayHumansImmunoassayLuminescent MeasurementsPandemicsPneumonia, ViralSARS-CoV-2Sensitivity and SpecificitySerologic TestsAntibodies, Viral

Identifiers

PMID32611558
PMCPMC7327913

What OpenQuestion holds

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LicenceCC BY-NC
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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.