An inflammatory cytokine signature predicts COVID-19 severity and survival

  1. Diane Marie Del Valle
  2. Seunghee Kim-Schulze
  3. Hsin-Hui Huang
  4. Noam D. Beckmann
  5. Sharon Nirenberg
  6. Bo Wang
  7. Yonit Lavin
  8. Talia H. Swartz
  9. Deepu Madduri
  10. Aryeh Stock
  11. Thomas U. Marron
  12. Hui Xie
  13. Manishkumar Patel
  14. Kevin Tuballes
  15. Oliver Van Oekelen
  16. Adeeb Rahman
  17. Patricia Kovatch
  18. Judith A. Aberg
  19. Eric Schadt
  20. Sundar Jagannath
  21. Madhu Mazumdar
  22. Alexander W. Charney
  23. Adolfo Firpo-Betancourt
  24. Damodara Rao Mendu
  25. Jeffrey Jhang
  26. David Reich
  27. Keith Sigel
  28. Carlos Cordon-Cardo
  29. Marc Feldmann
  30. Samir Parekh
  31. Miriam Merad
  32. Sacha Gnjatic

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Abstract

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  1. Version published to 10.1038/s41591-020-1051-9
  2. ScreenIT

    SciScore for 10.1101/2020.05.28.20115758: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Patient information and data source: This study was reviewed and approved by the Mount Sinai Institutional Review Board.
    Consent: A waiver of informed consent was obtained to query the patient electronic medical record.
    Randomizationnot detected.
    Blindingnot detected.
    Power AnalysisA two-sided log-rank test with an overall sample size of 674 patients (337 in each group) achieves 80% power at a 0.05 type I error to detect a hazard ratio of 2.29 when the proportion of death in the low group is 20%.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    We first validated IL-6, IL-8, and TNF-α detection by ELLA at the Mount Sinai Human Immune Monitoring Center using plasma from multiple myeloma patients undergoing immunotherapies such as CAR-T cells and bispecific antibodies, known to elicit cytokine release storm.
    IL-8
    suggested: None
    TNF-α
    suggested: None
    Software and Algorithms
    SentencesResources
    The sample size calculation was conducted using PASS software.
    PASS
    suggested: (PASS, RRID:SCR_005490)
    The analyses were performed using GraphPad Prism 8.4.2.,
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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.

  3. Version published to 10.1101/2020.05.28.20115758 on medRxiv