The role of interleukin‐6 in monitoring severe case of coronavirus disease 2019

  1. Tao Liu
  2. Jieying Zhang
  3. Yuhui Yang
  4. Hong Ma
  5. Zhenyu Li
  6. Jiaoyue Zhang
  7. Ji Cheng
  8. Xiaoyun Zhang
  9. Yanxia Zhao
  10. Zihan Xia
  11. Liling Zhang
  12. Gang Wu
  13. Jianhua Yi

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Abstract

Progression to severe disease is a difficult problem in treating coronavirus disease 2019 (COVID‐19). The purpose of this study is to explore changes in markers of severe disease in COVID‐19 patients. Sixty‐nine severe COVID‐19 patients were included. Patients with severe disease showed significant lymphocytopenia. Elevated level of lactate dehydrogenase (LDH), C‐reactive protein (CRP), ferritin, and D‐dimer was found in most severe cases. Baseline interleukin‐6 (IL‐6) was found to be associated with COVID‐19 severity. Indeed, the significant increase of baseline IL‐6 was positively correlated with the maximal body temperature during hospitalization and with the increased baseline of CRP, LDH, ferritin, and D‐dimer. High baseline IL‐6 was also associated with more progressed chest computed tomography (CT) findings. Significant decrease in IL‐6 and improved CT assessment was found in patients during recovery, while IL‐6 was further increased in exacerbated patients. Collectively, our results suggest that the dynamic change in IL‐6 can be used as a marker for disease monitoring in patients with severe COVID‐19.

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  1. Version published to 10.15252/emmm.202012421
  2. ScreenIT

    SciScore for 10.1101/2020.03.01.20029769: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Research Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology.
    Consent: Verbal consent was obtained from patients before the enrollment.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analyses were performed by Graphpad Prism (version 5.0) and SPSS 26.0 (IBM SPSS Statistics 26.0).
    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: 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 10.1101/2020.03.01.20029769 on medRxiv