Clinical validation of a point-of-care antibody test for COVID-19

  1. Monila Patel
  2. Yogesh Lakhotia
  3. Sneha Shah
  4. Nilay Suthar
  5. Cherry Shah
  6. Sankarrao Kola
  7. Radha Rangarajan

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Abstract

The objective of this study was to evaluate the performance of a lateral flow antibody test for COVID-19, approved for use in India. Although many point-of-care antibody tests are available globally, they have been subjected to limited clinical validation. This has led to suboptimal outcomes in the field, where antibody tests play a significant role in tracking the immunity of individuals and communities. In this study an antibody test, ImmunoQuick that recognizes antibodies to the Nucleocapsid and Spike proteins of SARS CoV-2 was tested in 100 symptomatic patients with a positive or negative diagnosis of COVID-19, based on RT-PCR results. The overall sensitivity of the test was found to be 86.1% (95% CI: 76.4% to 92.8%) and specificity 100% (95% confidence interval: 73.5% to 100%). The sensitivity reached a peak of 95.7% with samples taken 17 days after the onset of symptoms. Overall, the sensitivity and specificity of the test are sufficient for assessing seroprevalence.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.16.20248303: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the ethics committee of Smt NHL Municipal Medical College, where the M.P serves on the Faculty.
    Consent: Study design: Informed consent was taken from eligible individuals in writing.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    These samples were then processed using an Enzyme-linked immunosorbent assay (ELISA) kit for total antibodies (COVIDscreen Plus) from Trivitron Healthcare Pvt. Ltd.
    Trivitron Healthcare
    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.

  2. Version published to 10.1101/2020.12.16.20248303v1 on medRxiv