Evaluation of six different rapid methods for nucleic acid detection of SARS‐COV‐2 virus

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Abstract

In the current coronavirus disease 2019 (COVID‐19) pandemic there is a mass screening of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) happening around the world due to the extensive spread of the infections. There is a high demand for rapid diagnostic tests to expedite the identification of cases and to facilitate early isolation and control spread. Hence this study evaluates six different rapid nucleic acid detection assays that are commercially available for SARS‐CoV‐2 virus detection. Nasopharyngeal samples were collected from 4981 participants and were tested for the SARS‐CoV‐2 virus by the gold standard real‐time reverse‐transcription polymerase chain reaction (RT‐PCR) method and with one of these six rapid methods of detection. Evaluation of the rapid nucleic acid detection assays was done by comparing the results of these rapid methods with the gold standard RT‐qPCR results for SARS‐COV‐2 detection. AQ‐TOP had the highest sensitivity (98%) and a strong kappa value of 0.943 followed by Genechecker and Abbot ID NOW. The POCKIT (ii RT‐PCR) assay had the highest test accuracy of 99.29% followed by Genechecker and Cobas Liat. Atila iAMP showed the highest percentage of invalid reports (35.5%) followed by AQ‐TOP with 6% and POCKIT with 3.7% of invalid reports. Genechecker system, Abbott ID NOW, and Cobas Liat were found to have the best performance and agreement when compared with the standard RT‐PCR for COVID‐19 detection. With further research, these rapid tests have the potential to be employed in large‐scale screening of COVID‐19.

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  1. SciScore for 10.1101/2021.04.15.21255533: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: After getting informed consent from the participants nasopharyngeal swabs (NPS) were collected from the study participants.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    SPSS statistical software was used for all statistical analysis.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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.