Validation of the RT-LAMP assay in a large cohort of nasopharyngeal swab samples shows that it is a useful screening method for detecting SARS-CoV-2 and its VOC variants

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Abstract

The COVID-19 pandemic is challenging the global supply chain and equipment needed for mass testing with RT-qPCR, the gold standard for SARS-CoV-2 diagnosis. Here, we propose the RT-LAMP assay as an additional strategy for rapid virus diagnosis. However, its validation as a diagnostic method remains uncertain. In this work, we validated the RT-LAMP assay in 1,266 nasopharyngeal swab samples with confirmed diagnosis by CDC 2019-nCoV RT-qPCR. Our cohort was divided, the first (n=984) was used to evaluate two sets of oligonucleotides (S1 and S3) and the second (n=281) to determine whether RT-LAMP could detect samples with several types of variants. This assay can identify positive samples by color change or fluorescence within 40 minutes and shows high concordance with RT-qPCR in samples with CT ≤35. Also, S1 and S3 are able to detect SARS-CoV-2 with a sensitivity of 68.4% and 65.8%, and a specificity of 98.9% and 97.1%, respectively. Furthermore, RT-LAMP assay identified 279 sequenced samples as positive (99.3% sensitivity) corresponding to the Alpha, Beta, Gamma, Delta, Epsilon, Iota, Kappa, Lambda, Mu and Omicron variants. In conclusion, RT-LAMP is able to identify SARS-CoV-2 with good sensitivity and excellent specificity, including all VOC, VOI, VUM and FMV variants.

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  1. SciScore for 10.1101/2022.02.15.22270954: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Clinical samples: This cross-sectional, observational study was approved by the Institutional Ethics Committee of the National Institute of Genomic Medicine (INMEGEN) (CEI / 1479/20 and CEI 2020/21).
    Consent: After signing the informed consent, nasopharyngeal swab samples were collected from 984 patients and collected in a 15 ml conical tube with 3 ml of sterile viral transport medium (VTM).
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Other statistical analyzes were performed using GraphPad Prism 7.0 software and IBM SPSS Statistics version 24.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    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: We detected the following sentences addressing limitations in the study:
    This is why it’s important to underline that “bad but cheap” tests can be diagnostically useful, assuming that the tests’ limitations are carefully evaluated (70, 71). In our study, however, the specificity of RT-LAMP was 98.9% for the S1 oligonucleotide set and 97.1 % for the S3 oligonucleotide set, indicating that the test is an excellent method for detecting SARS-CoV-2 despite the presence of other interfering molecules isolated during RNA extraction from nasopharyngeal swab samples. Furthermore, based on the positive and negative predictive value of our data, the RT-LAMP assay could be used for massive COVID-19 screening. As a consequence, the strong positive predictive value (99.1 % for set S1 and 97.6 % for set S3, respectively) indicates that patients who have a positive RT-LAMP test actually have the condition; whereas the negative predictive value for both oligonucleotide sets was modest, this suggests that even if the test was negative, there is still a risk of infection. As a result, we suggest that using a ROC curve analysis to directly compare the cost/benefit of the RT-LAMP assay and other diagnostic procedures is appropriate for making diagnostic decisions. Although, other study describes greater sensitivity results for the RT-LAMP assay (20 RNA copies per reaction), comparable to RT-qPCR test (72). In this regard, we suggest that the small number of samples and the design of primers based on only 130 fully aligned SARS-CoV-2 genomes are important limiting fact...

    Results from TrialIdentifier: No clinical trial numbers were referenced.


    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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.

    Results from scite Reference Check: We found no unreliable references.


    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.