Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements

  1. Kai Zhou
  2. Yaoting Sun
  3. Lu Li
  4. Zelin Zang
  5. Jing Wang
  6. Jun Li
  7. Junbo Liang
  8. Fangfei Zhang
  9. Qiushi Zhang
  10. Weigang Ge
  11. Hao Chen
  12. Xindong Sun
  13. Liang Yue
  14. Xiaomai Wu
  15. Bo Shen
  16. Jiaqin Xu
  17. Hongguo Zhu
  18. Shiyong Chen
  19. Hai Yang
  20. Shigao Huang
  21. Minfei Peng
  22. Dongqing Lv
  23. Chao Zhang
  24. Haihong Zhao
  25. Luxiao Hong
  26. Zhehan Zhou
  27. Haixiao Chen
  28. Xuejun Dong
  29. Chunyu Tu
  30. Minghui Li
  31. Yi Zhu
  32. Baofu Chen
  33. Stan Z. Li
  34. Tiannan Guo

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  1. Version published to 10.1016/j.csbj.2021.06.022
  2. ScreenIT

    SciScore for 10.1101/2020.07.28.20163022: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Cytokines and lymphocyte subsets were measured using BD FACSCantoTM II within 6 hours.
    BD FACSCantoTM
    suggested: None
    In the feature selection step, the standard GA method in the Python deap library is used.
    Python
    suggested: (IPython, RRID:SCR_001658)
    The SVM model with ‘rbf’ kernal in Python’s sklearn library was employed.
    Python’s
    suggested: (PyMVPA, RRID:SCR_006099)
    SPSS (version 19.0) was used for baseline characteristics.
    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. ScreenIT

    SciScore for 10.1101/2020.07.28.20163022: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.RandomizationWe randomly generated feature combinations and loaded the data with selected features.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableThis cohort has 55.6% male in the severe COVID-19 group and 52.8% male in the nonsevere COVID-19 group.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    ACKNOWLEDGMENTS This work is supported by grants from Tencent Foundation (2020), National Natural Science Foundation of China (81972492, 21904107, 81672086), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04).
    Society Advancement Program
    suggested: None

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  4. Version published to 10.1101/2020.07.28.20163022 on medRxiv