Suboptimal Biological Sampling as a Probable Cause of False-Negative COVID-19 Diagnostic Test Results

  1. Natalie N Kinloch
  2. Gordon Ritchie
  3. Chanson J Brumme
  4. Winnie Dong
  5. Weiyan Dong
  6. Tanya Lawson
  7. R Brad Jones
  8. Julio S G Montaner
  9. Victor Leung
  10. Marc G Romney
  11. Aleksandra Stefanovic
  12. Nancy Matic
  13. Christopher F Lowe
  14. Zabrina L Brumme

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Abstract

False-negative severe acute respiratory syndrome coronavirus 2 test results can negatively impact the clinical and public health response to coronavirus disease 2019 (COVID-19). We used droplet digital polymerase chain reaction (ddPCR) to demonstrate that human DNA levels, a stable molecular marker of sampling quality, were significantly lower in samples from 40 confirmed or suspected COVID-19 cases that yielded negative diagnostic test results (ie, suspected false-negative test results) compared with a representative pool of 87 specimens submitted for COVID-19 testing. Our results support suboptimal biological sampling as a contributor to false-negative COVID-19 test results and underscore the importance of proper training and technique in the collection of nasopharyngeal specimens.

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  1. Version published to 10.1093/infdis/jiaa370
  2. ScreenIT

    SciScore for 10.1101/2020.05.05.20091728: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Providence Health Care/University of British Columbia and the Simon Fraser University Research Ethics Boards.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Droplets were analyzed on a QX200 Droplet Reader (BioRad) using QuantaSoft software (BioRad, version 1.7.4).
    QuantaSoft
    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.

  3. Version published to 10.1101/2020.05.05.20091728 on medRxiv