Assessment of sample pooling for clinical SARS-CoV-2 testing

  1. Sara B Griesemer
  2. Greta Van Slyke
  3. Kirsten St. George

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Abstract

Accommodating large increases in sample workloads has presented one of the biggest challenges to clinical laboratories during the COVID-19 pandemic. Despite the implementation of new automated detection systems, and previous efficiencies such as barcoding, electronic data transfer and extensive robotics, throughput capacities have struggled to meet the demand. Sample pooling has been suggested as an additional strategy to further address this need. The greatest concern with this approach in a clinical setting is the potential for reduced sensitivity, particularly the risk of false negative results when weak positive samples are pooled. To investigate this possibility, detection rates in pooled samples were evaluated, with extensive assessment of pools containing weak positive specimens. Additionally, the frequency of occurrence of weak positive samples across ten weeks of the pandemic were reviewed. Weak positive specimens were detected in all five-sample pools but failed to be detected in four of the 24 nine-sample pools tested. Weak positive samples comprised an average 16.5% of the positive specimens tested during the pandemic thus far, slightly increasing in frequency during later weeks. Other aspects of the testing process should be considered, however, such as accessioning and reporting, which are not streamlined and may be complicated by pooling procedures. Therefore, the impact on the entire laboratory process needs to be carefully assessed prior to implementing such a strategy.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.26.118133: (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.
    • No funding statement was detected.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2020.05.26.118133 on bioRxiv