SARS-CoV-2 RT-qPCR Test Detection Rates Are Associated with Patient Age, Sex, and Time since Diagnosis

  1. Matan Levine-Tiefenbrun
  2. Idan Yelin
  3. Hedva Uriel
  4. Jacob Kuint
  5. Licita Schreiber
  6. Esma Herzel
  7. Rachel Katz
  8. Amir Ben-Tov
  9. Sivan Gazit
  10. Tal Patalon
  11. Gabriel Chodick
  12. Roy Kishony

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Abstract

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  1. Version published to 10.1016/j.jmoldx.2021.10.010
  2. ScreenIT

    SciScore for 10.1101/2020.10.30.20222935: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval: The study protocol was approved by the ethics committee of Maccabi Healthcare Services, Tel-Aviv, Israel.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data collection: Anonymized clinical records of SARS-CoV-2 RT-qPCR test results (test reports) were retrieved by Maccabi Healthcare Services (MHS) for the period between February 8th and September 24th 2020.
    Maccabi Healthcare
    suggested: None
    Linear regression: Linear regression of Ct values for each fluorescence channel was performed using Python’s statsmodels library.
    Python’s
    suggested: (PyMVPA, RRID:SCR_006099)
    Statistical significance for differences in FNR between groups was tested using a two-sided Fisher’s exact test (SciPy in Python).
    SciPy
    suggested: (SciPy, RRID:SCR_008058)
    Python
    suggested: (IPython, RRID:SCR_001658)

    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: We detected the following sentences addressing limitations in the study:
    Our study has several limitations. First, we treat all positive tests as true positive. While errors may occur, the rate of false-positive results is very low2–5 and we do not expect it to significantly affect our results. Future studies can further improve the reliability of confirmation of positive cases by combining PCR test results with serology tests. Second, we treat negative results at the end of test series as ‘true-negative’, while it is possible that if the test series were continued additional positive tests might have been detected. Again, we do not expect this to significantly affect our results: most series in our study end with two consecutive negative results, and the chances for two consecutive false-negative tests are very low. Moreover, this bias will mostly affect the calculated false-negative rate at later days after diagnosis. Third, as viral loads after infection may first increase and only later decrease, it is possible that false-negative rates follow an opposite pattern: first decreasing and only later increasing. Analysing our cohort, we could only identify the later phase of increasing false-negative rate. However, it is possible that with different cohorts or inclusion criteria, both phases can be observed. Fourth, it will be interesting to see how changing the way Ct is calculated can fine-tune the way positive and negative results are determined based on conflicting results of the genes. Finally, we emphasize that most of the patients in the stu...

    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 10.1101/2020.10.30.20222935 on medRxiv