Evaluations of serological test in the diagnosis of 2019 novel coronavirus (SARS-CoV-2) infections during the COVID-19 outbreak

Lin, Dachuan; Liu, Lei; Zhang, Mingxia; Hu, Yunlong; Yang, Qianting; Guo, Jiubiao; Dai, Youchao; Xu, Yuzhong; Cai, Yuchen; Chen, Xinchun; Huang, Kaisong; Zhang, Zheng

doi:10.1101/2020.03.27.20045153

Cited by 55 publications

(79 citation statements)

References 20 publications

(13 reference statements)

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“…The electronic search revealed 115 articles from PubMed, 72 from medRxiv and 12 from bioRxiv, from which we identified 38 eligible studies after scrutiny [25][26][27][28][29][30][31][32][33][34][35][36][37] (Figure 1). These include in total 7,848 individuals (3,522 COVID-19 cases and 4,326 healthy, or non COVID-19, individuals).…”

Section: Resultsmentioning

confidence: 99%

Antibody tests in detecting SARS-CoV-2 infection: a meta-analysis

Kontou

Braliou

Dimou

et al. 2020

Preprint

124

129

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With the emergence of SARS-CoV-2 and the associated Coronavirus disease 2019 (COVID-19), there is an imperative need for diagnostic tests that can identify the infection. Although Nucleic Acid Test (NAT) is considered to be the gold standard, serological tests based on antibodies could be very helpful. However, individual studies measuring the accuracy of the various tests are usually underpowered and inconsistent, thus, a comparison of different tests is needed. We performed a systematic review and meta-analysis following the PRISMA guidelines. We conducted the literature search in PubMed, medRxiv and bioRxiv. For the statistical analysis we used the bivariate method for meta-analysis of diagnostic tests pooling sensitivities and specificities. We evaluated IgM and IgG tests based on Enzyme-linked immunosorbent assay (ELISA), Chemiluminescence Enzyme Immunoassays (CLIA), Fluorescence Immunoassays (FIA) and the point-of-care (POC) Lateral Flow Immunoassays (LFIA) that are based on immunochromatography. In total, we identified 38 eligible studies that include data from 7,848 individuals. The analyses showed that tests using the S antigen are more sensitive than N antigen-based tests. IgG tests perform better compared to IgM ones, and show better sensitivity when the samples were taken longer after the onset of symptoms. Moreover, irrespective of the method, a combined IgG/IgM test seems to be a better choice in terms of sensitivity than measuring either antibody type alone. All methods yielded high specificity with some of them (ELISA and LFIA) reaching levels around 99%. ELISA- and CLIA-based methods performed better in terms of sensitivity (90-94%) followed by LFIA and FIA with sensitivities ranging from 80% to 86%. ELISA tests could be a safer choice at this stage of the pandemic. POC tests (LFIA), that are more attractive for large seroprevalence studies show high specificity but lower sensitivity and this should be taken into account when designing and performing seroprevalence studies.

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Section: Resultsmentioning

confidence: 99%

Antibody tests in detecting SARS-CoV-2 infection: a meta-analysis

Kontou

Braliou

Dimou

et al. 2020

Preprint

124

129

View full text Add to dashboard Cite

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“…Many serological enzyme-linked immunosorbent assays (ELISA) have been recently developed to detect anti-SARS-CoV-2 antibodies. However, these assays have four important limitations [4][5][6][7][8][9][10][11][12][13][14][15] . First, they lack the ability to detect antibodies at early stages of infection.…”

Section: Mainmentioning

confidence: 99%

Ultra-Sensitive High-Resolution Profiling of Anti-SARS-CoV-2 Antibodies for Detecting Early Seroconversion in COVID-19 Patients

Norman

Gilboa

Ogata

et al. 2020

Preprint

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The COVID-19 pandemic continues to infect millions of people worldwide. In order to curb its spread and reduce morbidity and mortality, it is essential to develop sensitive and quantitative methods that identify infected individuals and enable accurate population-wide screening of both past and present infection. Here we show that Single Molecule Array assays detect seroconversion in COVID-19 patients as soon as one day after symptom onset using less than a microliter of blood. This multiplexed assay format allows us to quantitate IgG, IgM and IgA immunoglobulins against four SARS-CoV-2 targets, thereby interrogating 12 antibody isotype-viral protein interactions to give a high resolution profile of the immune response. Using a cohort of samples collected prior to the outbreak as well as samples collected during the pandemic, we demonstrate a sensitivity of 86% and a specificity of 100% during the first week of infection, and 100% sensitivity and specificity thereafter. This assay should become the gold standard for COVID19 serological profiling and will be a valuable tool for answering important questions about the heterogeneity of clinical presentation seen in the ongoing pandemic.

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“…Currently available serological tests have high specificity and relatively low sensitivity [3,4], as expected for this type of testing [5]. Studies which evaluated the tests on severe cases report high specificity and sensitivity [6], however, the discriminatory power of the tests is less clear for mild cases and even asymptomatic cases [7].…”

Section: Introductionmentioning

confidence: 99%

Estimating cumulative incidence of SARS-CoV-2 with imperfect serological tests: exploiting cutoff-free approaches

Bouman

Riou

Bonhoeffer

et al. 2020

Preprint

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Large-scale serological testing in the population is essential to determine the true extent of the current Coronavirus pandemic. Serological tests measure antibody responses against pathogens and define cutoff levels that dichotomize the quantitative test measures into sero-positives and negatives. With the imperfect tests that are currently available to test for past SARS-CoV-2 infection, the fraction of seropositive individuals in serosurveys is a biased estimator of seroprevalence and is usually corrected post-hoc to account for the sensitivity and specificity. Here we use a likelihood-based inference method -previously called mixture models -for the estimation of the seroprevalence that does not require to define cutoffs by integrating the quantitative test measures directly into the statistical inference procedure. We confirm that this likelihood-based method outperforms the methods based on cutoffs and post-hoc corrections leading to less variation in point-estimates of the seroprevalence and its temporal trend. We illustrate how the likelihood-based method can be used to optimize the design of serosurveys with imperfect serological tests. We also provide guidance on the number of control and case sera that are required to quantify the test's ambiguity sufficiently to enable the reliable estimation of the seroprevalence. Lastly, we show how this approach can be used to identify classes of case sera with an unknown distribution of quantitative test measures that have not been used for test validation. An R-package with the likelihood and power analysis functions is provided. Our study advocates to using serological tests without cutoffs, especially if they are used to determine parameters characterizing populations rather than individuals. This approach circumvents some of the shortcomings of cutoff-based methods with post-hoc correction at exactly the low seroprevalence levels and test accuracies that we are currently facing in COVID-19 serosurveys. Author SummaryAs other pathogens, SARS-CoV-2 elicits antibody responses in infected people that can be detected in their blood serum as early as a week after the infection until long after recovery. The presence of SARS-CoV-2 specific antibodies can therefore be used as a marker of past infection, and the prevalence of seropositive people, i.e. people with specific antibodies, is a key measure to determine the extent of the Coronavirus pandemic. The serological tests, however, are usually not perfect, yielding false positive and negative results.Here we exploit an approach that refrains from classifying people as seropositive or negative, but rather compares the antibody level to that of confirmed cases and controls. This approach leads to more reliable seroprevalence estimates, especially for the low prevalence and low test accuracies that we face during the current Coronavirus pandemic. We also show how this approach can be extended to infer the presence of cases that have not been used for validating the test, such as people that underwent a mild or even asym...

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Evaluations of serological test in the diagnosis of 2019 novel coronavirus (SARS-CoV-2) infections during the COVID-19 outbreak

Cited by 55 publications

References 20 publications

Antibody tests in detecting SARS-CoV-2 infection: a meta-analysis

Antibody tests in detecting SARS-CoV-2 infection: a meta-analysis

Ultra-Sensitive High-Resolution Profiling of Anti-SARS-CoV-2 Antibodies for Detecting Early Seroconversion in COVID-19 Patients

Estimating cumulative incidence of SARS-CoV-2 with imperfect serological tests: exploiting cutoff-free approaches

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