Clinical Characterisation of Eleven Lateral Flow Assays for Detection of COVID-19 Antibodies in a Population

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Abstract

Importance

Serological assays can help diagnose and determine the rate of SARS-CoV-2 infections in a population.

Objective

We characterized and compared 11 different lateral flow assays for their performance in diagnostic or epidemiological settings.

Design, Setting, Participants

We used two cohorts to determine the specificity: (i) up to 350 blood donor samples from past influenza seasons and (ii) up to 110 samples which tested PCR negative for SARS-CoV-2 during the first wave of SARS-CoV-2 infections in Switzerland. The sensitivity was determined using up to 370 samples which tested PCR positive for SARS-CoV-2 during the same time and is representative for age distribution and severity.

Main Outcome

We found a single test usable for epidemiological studies in the current low-prevalence setting, all other tests showed lacking sensitivity or specificity for a usage in either epidemiological or diagnostic setting. However, orthogonal testing by combining two tests without common cross-reactivities makes testing in a low-prevalence setting feasible.

Results

Nine out of the eleven tests showed specificities below 99%, only five of eleven tests showed sensitivities comparable to established ELISAs, and only one fulfilled both criteria. Contrary to previous results from lab assays, five tests measured an IgM response in >80% of the samples. We found no common cross-reactivities, which allows orthogonal testing schemes for five tests of sufficient sensitivities.

Conclusions and Relevance

This study emphasizes the need for large and diverse negative cohorts when determining specificities, and for diverse and representative positive samples when determining sensitivities of lateral flow assays for SARS-CoV-2 infections. Failure to adhere to statistically relevant sample sizes or cohorts exclusively made up of hospitalised patients fails to accurately capture the performance of these assays in epidemiological settings. Our results allow a rational choice between tests for different use cases.

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  1. SciScore for 10.1101/2020.08.18.20177204: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Antibodies
    SentencesResources
    We considered a test valid if its control band was present, and we considered a valid test positive for the respective antibody if the SARS-CoV-2 specific IgM, IgG or IgM/IgG band was detected in the sample.
    SARS-CoV-2 specific IgM, IgG
    suggested: None

    Results from OddPub: Thank you for sharing your code.


    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.


    Results from JetFighter: We did not find any issues relating to colormaps.


    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.