Saliva is Comparable to Nasopharyngeal Swabs for Molecular Detection of SARS-CoV-2

  1. Cody Callahan
  2. Sarah Ditelberg
  3. Sanjucta Dutta
  4. Nancy Littlehale
  5. Annie Cheng
  6. Kristin Kupczewski
  7. Danielle McVay
  8. Stefan Riedel
  9. James E. Kirby
  10. Ramy Arnaout

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Abstract

In general, the most accurate COVID-19 testing is hands-on and uncomfortable, requiring trained staff and a “brain-tickling” nasopharyngeal swab. Saliva would be much easier on both fronts, since patients could collect it themselves, and it is after all just spit.

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  1. Version published to 10.1128/spectrum.00162-21
  2. ScreenIT

    SciScore for 10.1101/2021.04.21.21255621: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Institutional review: This study was reviewed and approved by institutional review board at the Beth Israel Deaconess Medical Center (BIDMC; IRB protocol no. 2020P000769).
    Consent: Trial participants and sample collection: Informed consent was obtained from English-speaking adults presenting for either initial or followup testing for COVID-19 at BIDMC and Beth Israel Deaconess Chelsea drive-through collection sites.
    Field Sample Permit: Stability testing: Saliva specimens were collected as described above and stored at 4°C pending results of the paired NP specimen.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Abbott multi-collect transport media, part of the Abbott Multi-Collect Specimen Collection Kit, catalog no. 09K12-004; Abbott Laboratories, Abbott Park, IL).
    Abbott Laboratories
    suggested: None
    At the start of the study, treated, untreated, and a 1mL aliquot of NP sample from the same participant were briefly vortexed (2-5 seconds) and amplified using Abbott m2000 RealTime SARS-CoV-2 Assay on an Abbott m2000 RealTime System or the Abbott Alinity m system.
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    Software: We used Python (v3.6-3.8) and its NumPy, SciPy, Matplotlib, Pandas, and ct2vl libraries for the above analyses and related visualizations.
    Python
    suggested: (IPython, RRID:SCR_001658)
    NumPy
    suggested: (NumPy, RRID:SCR_008633)
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    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.

  3. Version published to 10.1101/2021.04.21.21255621 on medRxiv