Increasing test specificity without impairing sensitivity: lessons learned from SARS-CoV-2 serology

This article has been Reviewed by the following groups

Read the full article See related articles

Abstract

Serological tests are widely used in various medical disciplines for diagnostic and monitoring purposes. Unfortunately, the sensitivity and specificity of test systems are often poor, leaving room for false-positive and false-negative results. However, conventional methods were used to increase specificity and decrease sensitivity and vice versa. Using SARS-CoV-2 serology as an example, we propose here a novel testing strategy: the ‘sensitivity improved two-test’ or ‘SIT²’ algorithm.

Methods

SIT² involves confirmatory retesting of samples with results falling in a predefined retesting zone of an initial screening test, with adjusted cut-offs to increase sensitivity. We verified and compared the performance of SIT² to single tests and orthogonal testing (OTA) in an Austrian cohort (1117 negative, 64 post-COVID-positive samples) and validated the algorithm in an independent British cohort (976 negatives and 536 positives).

Results

The specificity of SIT² was superior to single tests and non-inferior to OTA. The sensitivity was maintained or even improved using SIT² when compared with single tests or OTA. SIT² allowed correct identification of infected individuals even when a live virus neutralisation assay could not detect antibodies. Compared with single testing or OTA, SIT² significantly reduced total test errors to 0.46% (0.24–0.65) or 1.60% (0.94–2.38) at both 5% or 20% seroprevalence.

Conclusion

For SARS-CoV-2 serology, SIT² proved to be the best diagnostic choice at both 5% and 20% seroprevalence in all tested scenarios. It is an easy to apply algorithm and can potentially be helpful for the serology of other infectious diseases.

Article activity feed

  1. SciScore for 10.1101/2020.11.05.20226449: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: All included participants gave written informed consent to donate their samples for scientific purposes.
    IRB: It was reviewed and approved by the ethics committee of the Medical University of Vienna (1424/2020).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Calibrators and controls of both tests are traceable to a specific antibody against the SARS-CoV-2 receptor-binding domain (CR3022).
    SARS-CoV-2 receptor-binding domain (CR3022
    suggested: None
    Software and Algorithms
    SentencesResources
    The Abbott SARS-CoV-2 assay detects IgG-antibodies against NC in a chemiluminescence microparticle assay (CMIA) on Abbott ARCHITECT® i2000sr platforms (Abbott Laboratories, Chicago, USA).
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    Abbott Laboratories
    suggested: None

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).


    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.