How to best test suspected cases of COVID-19: an analysis of the diagnostic performance of RT-PCR and alternative molecular methods for the detection of SARS-CoV-2

  1. Adrian Mironas
  2. David Jarrom
  3. Evan Campbell
  4. Jennifer Washington
  5. Sabine Ettinger
  6. Ingrid Wilbacher
  7. Gottfried Endel
  8. Hrvoje Vrazic
  9. Susan Myles
  10. Matthew Prettyjohns

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Abstract

As COVID-19 testing is rolled out increasingly widely, the use of a range of alternative testing methods will be beneficial in ensuring testing systems are resilient and adaptable to different clinical and public health scenarios. Here, we compare and discuss the diagnostic performance of a range of different molecular assays designed to detect the presence of SARS-CoV-2 infection in people with suspected COVID-19. Using findings from a systematic review of 103 studies, we categorised COVID-19 molecular assays into 12 different test classes, covering point-of-care tests, various alternative RT-PCR protocols, and alternative methods such as isothermal amplification. We carried out meta-analyses to estimate the diagnostic accuracy and clinical utility of each test class. We also estimated the positive and negative predictive values of all diagnostic test classes across a range of prevalence rates. Using previously validated RT-PCR assays as a reference standard, 11 out of 12 classes showed a summary sensitivity estimate of at least 92% and a specificity estimate of at least 99%. Several diagnostic test classes were estimated to have positive predictive values of 100% throughout the investigated prevalence spectrum, whilst estimated negative predictive values were more variable and sensitive to disease prevalence. We also report the results of clinical utility models that can be used to determine the information gained from a positive and negative test result in each class, and whether each test is more suitable for confirmation or exclusion of disease. Our analysis suggests that several tests exist that are suitable alternatives to standard RT-PCR and we discuss scenarios in which these could be most beneficial, such as where time to test result is critical or, where resources are constrained. However, we also highlight methodological concerns with the design and conduct of many included studies, and also the existence of likely publication bias for some test classes. Our results should be interpreted with these shortcomings in mind. Furthermore, our conclusions on test performance are limited to their use in symptomatic populations: we did not identify sufficient suitable data to allow analysis of testing in asymptomatic populations.

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    SciScore for 10.1101/2021.01.15.21249863: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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 10.1101/2021.01.15.21249863v1 on medRxiv