Evidence map›Paper›PMID 36394900›Full record

SynthesisThe Cochrane database of systematic reviews2022

Antibody tests for identification of current and past infection with SARS-CoV-2.

Tilly Fox, Julia Geppert, Jacqueline Dinnes, Katie Scandrett, Jacob Bigio, Giorgia Sulis, Dineshani Hettiarachchi, Yasith Mathangasinghe, Praveen Weeratunga, Dakshitha Wickramasinghe and 19 more

Open access · bronzeAbstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in The Cochrane database of systematic reviews, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
77citing papers in PubMed, 6 pooled it
14.7field-weighted citation impact, top 1% 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

77 citing papers in PubMed, 6 syntheses or guidelines pooled it, 202 citations in OpenAlex.

  1. Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection.The Cochrane database of systematic reviews · 2025
    Pooled it
  2. Pooled it
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  6. Nirmatrelvir combined with ritonavir for preventing and treating COVID-19.The Cochrane database of systematic reviews · 2023
    Pooled it
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17 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

29 authors at 16 institutions in 8 countries.

Tilly FoxDepartment of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK.
Julia GeppertDivision of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Jacqueline DinnesTest Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
Katie ScandrettTest Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
Jacob BigioResearch Institute of the McGill University Health Centre, Montreal, Canada.
Giorgia SulisDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.
Dineshani HettiarachchiDepartment of Anatomy Genetics and Biomedical Informatics, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka.
Yasith MathangasingheDepartment of Anatomy Genetics and Biomedical Informatics, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka.
Praveen WeeratungaDepartment of Clinical Medicine, Faculty of Medicine, University of Colombo, Colombo, Sri Lanka.
Dakshitha WickramasingheFaculty of Medicine, University of Colombo, Colombo, Sri Lanka.
Hanna BergmanCochrane Response, Cochrane, London, UK.
Brian S BuckleyCochrane Response, Cochrane, London, UK.
Katrin ProbynCochrane Response, Cochrane, London, UK.
Yanina SguasseroCochrane Response, Cochrane, London, UK.
Clare DavenportTest Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
Jane CunninghamGlobal Malaria Programme, World Health Organization, Geneva, Switzerland.
Sabine DittrichFIND, Geneva, Switzerland.
Devy EmperadorFIND, Geneva, Switzerland.
Lotty HooftCochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht , Netherlands.
Mariska Mg LeeflangEpidemiology and Data Science, Amsterdam UMC location University of Amsterdam, Amsterdam, Netherlands.
Matthew Df McInnesDepartment of Radiology, University of Ottawa, Ottawa, Canada.
René SpijkerMedical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands.
Thomas StruyfDepartment of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
Ann Van den BruelDepartment of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
Jan Y VerbakelDepartment of Public Health and Primary Care, KU Leuven, Leuven, Belgium.
Yemisi TakwoingiTest Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
Sian Taylor-PhillipsDivision of Health Sciences, Warwick Medical School, University of Warwick, Coventry, UK.
Jonathan J DeeksTest Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
Cochrane COVID-19 Diagnostic Test Accuracy GroupTest Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK.
Cochrane · GBUniversity of Colombo · LKKU Leuven · BEUniversity of Birmingham · GBFoundation for Innovative New Diagnostics · CHNIHR Birmingham Biomedical Research Centre · GBLiverpool School of Tropical Medicine · GBMcGill University · CAMcGill University Health Centre · CAUniversity Hospitals Birmingham NHS Foundation Trust · GBUniversity Medical Center Utrecht · NLUniversity of Amsterdam · NLUniversity of Ottawa · CAUniversity of Warwick · GBUtrecht University · NLWorld Health Organization · CH

Funding

Medical Research Council MC_PC_19078World Health Organization 001
6 · The paper itself

Abstract

backgroundThe diagnostic challenges associated with the COVID-19 pandemic resulted in rapid development of diagnostic test methods for detecting SARS-CoV-2 infection. Serology tests to detect the presence of antibodies to SARS-CoV-2 enable detection of past infection and may detect cases of SARS-CoV-2 infection that were missed by earlier diagnostic tests. Understanding the diagnostic accuracy of serology tests for SARS-CoV-2 infection may enable development of effective diagnostic and management pathways, inform public health management decisions and understanding of SARS-CoV-2 epidemiology.

objectivesTo assess the accuracy of antibody tests, firstly, to determine if a person presenting in the community, or in primary or secondary care has current SARS-CoV-2 infection according to time after onset of infection and, secondly, to determine if a person has previously been infected with SARS-CoV-2. Sources of heterogeneity investigated included: timing of test, test method, SARS-CoV-2 antigen used, test brand, and reference standard for non-SARS-CoV-2 cases. SEARCH

methodsThe COVID-19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) was searched on 30 September 2020. We included additional publications from the Evidence for Policy and Practice Information and Co-ordinating Centre (EPPI-Centre) 'COVID-19: Living map of the evidence' and the Norwegian Institute of Public Health 'NIPH systematic and living map on COVID-19 evidence'. We did not apply language restrictions. SELECTION CRITERIA: We included test accuracy studies of any design that evaluated commercially produced serology tests, targeting IgG, IgM, IgA alone, or in combination. Studies must have provided data for sensitivity, that could be allocated to a predefined time period after onset of symptoms, or after a positive RT-PCR test. Small studies with fewer than 25 SARS-CoV-2 infection cases were excluded. We included any reference standard to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction tests (RT-PCR), clinical diagnostic criteria, and pre-pandemic samples). DATA COLLECTION AND ANALYSIS: We use standard screening procedures with three reviewers. Quality assessment (using the QUADAS-2 tool) and numeric study results were extracted independently by two people. Other study characteristics were extracted by one reviewer and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test and, for meta-analysis, we fitted univariate random-effects logistic regression models for sensitivity by eligible time period and for specificity by reference standard group. Heterogeneity was investigated by including indicator variables in the random-effects logistic regression models. We tabulated results by test manufacturer and summarised results for tests that were evaluated in 200 or more samples and that met a modification of UK Medicines and Healthcare products Regulatory Agency (MHRA) target performance criteria. MAIN

resultsWe included 178 separate studies (described in 177 study reports, with 45 as pre-prints) providing 527 test evaluations. The studies included 64,688 samples including 25,724 from people with confirmed SARS-CoV-2; most compared the accuracy of two or more assays (102/178, 57%). Participants with confirmed SARS-CoV-2 infection were most commonly hospital inpatients (78/178, 44%), and pre-pandemic samples were used by 45% (81/178) to estimate specificity. Over two-thirds of studies recruited participants based on known SARS-CoV-2 infection status (123/178, 69%). All studies were conducted prior to the introduction of SARS-CoV-2 vaccines and present data for naturally acquired antibody responses. Seventy-nine percent (141/178) of studies reported sensitivity by week after symptom onset and 66% (117/178) for convalescent phase infection. Studies evaluated enzyme-linked immunosorbent assays (ELISA) (165/527; 31%), chemiluminescent assays (CLIA) (167/527; 32%) or lateral flow assays (LFA) (188/527; 36%). Risk of bias was high because of participant selection (172, 97%); application and interpretation of the index test (35, 20%); weaknesses in the reference standard (38, 21%); and issues related to participant flow and timing (148, 82%). We judged that there were high concerns about the applicability of the evidence related to participants in 170 (96%) studies, and about the applicability of the reference standard in 162 (91%) studies. Average sensitivities for current SARS-CoV-2 infection increased by week after onset for all target antibodies. Average sensitivity for the combination of either IgG or IgM was 41.1% in week one (95% CI 38.1 to 44.2; 103 evaluations; 3881 samples, 1593 cases), 74.9% in week two (95% CI 72.4 to 77.3; 96 evaluations, 3948 samples, 2904 cases) and 88.0% by week three after onset of symptoms (95% CI 86.3 to 89.5; 103 evaluations, 2929 samples, 2571 cases). Average sensitivity during the convalescent phase of infection (up to a maximum of 100 days since onset of symptoms, where reported) was 89.8% for IgG (95% CI 88.5 to 90.9; 253 evaluations, 16,846 samples, 14,183 cases), 92.9% for IgG or IgM combined (95% CI 91.0 to 94.4; 108 evaluations, 3571 samples, 3206 cases) and 94.3% for total antibodies (95% CI 92.8 to 95.5; 58 evaluations, 7063 samples, 6652 cases). Average sensitivities for IgM alone followed a similar pattern but were of a lower test accuracy in every time slot. Average specificities were consistently high and precise, particularly for pre-pandemic samples which provide the least biased estimates of specificity (ranging from 98.6% for IgM to 99.8% for total antibodies). Subgroup analyses suggested small differences in sensitivity and specificity by test technology however heterogeneity in study results, timing of sample collection, and smaller sample numbers in some groups made comparisons difficult. For IgG, CLIAs were the most sensitive (convalescent-phase infection) and specific (pre-pandemic samples) compared to both ELISAs and LFAs (P < 0.001 for differences across test methods). The antigen(s) used (whether from the Spike-protein or nucleocapsid) appeared to have some effect on average sensitivity in the first weeks after onset but there was no clear evidence of an effect during convalescent-phase infection. Investigations of test performance by brand showed considerable variation in sensitivity between tests, and in results between studies evaluating the same test. For tests that were evaluated in 200 or more samples, the lower bound of the 95% CI for sensitivity was 90% or more for only a small number of tests (IgG, n = 5; IgG or IgM, n = 1; total antibodies, n = 4). More test brands met the MHRA minimum criteria for specificity of 98% or above (IgG, n = 16; IgG or IgM, n = 5; total antibodies, n = 7). Seven assays met the specified criteria for both sensitivity and specificity. In a low-prevalence (2%) setting, where antibody testing is used to diagnose COVID-19 in people with symptoms but who have had a negative PCR test, we would anticipate that 1 (1 to 2) case would be missed and 8 (5 to 15) would be falsely positive in 1000 people undergoing IgG or IgM testing in week three after onset of SARS-CoV-2 infection. In a seroprevalence survey, where prevalence of prior infection is 50%, we would anticipate that 51 (46 to 58) cases would be missed and 6 (5 to 7) would be falsely positive in 1000 people having IgG tests during the convalescent phase (21 to 100 days post-symptom onset or post-positive PCR) of SARS-CoV-2 infection. AUTHORS'

conclusionsSome antibody tests could be a useful diagnostic tool for those in whom molecular- or antigen-based tests have failed to detect the SARS-CoV-2 virus, including in those with ongoing symptoms of acute infection (from week three onwards) or those presenting with post-acute sequelae of COVID-19. However, antibody tests have an increasing likelihood of detecting an immune response to infection as time since onset of infection progresses and have demonstrated adequate performance for detection of prior infection for sero-epidemiological purposes. The applicability of results for detection of vaccination-induced antibodies is uncertain.

Indexed as

COVID-19SARS-CoV-2Antibodies, ViralCOVID-19 VaccinesHumansImmunoglobulin GImmunoglobulin MPandemicsSeroepidemiologic StudiesAntibodies, ViralCOVID-19 VaccinesImmunoglobulin GImmunoglobulin M

Identifiers

PMID36394900
PMCPMC9671206
OpenAlexW4309183903

What OpenQuestion holds

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LicenceCC BY-NC
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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.