Clinical course and risk factors for mortality of COVID-19 patients with pre-existing cirrhosis: a multicentre cohort study

  1. Xiaolong Qi
  2. Yanna Liu
  3. Jitao Wang
  4. Jonathan A Fallowfield
  5. Jianwen Wang
  6. Xinyu Li
  7. Jindong Shi
  8. Hongqiu Pan
  9. Shengqiang Zou
  10. Hongguang Zhang
  11. Zhenhuai Chen
  12. Fujian Li
  13. Yan Luo
  14. Mei Mei
  15. Huiling Liu
  16. Zhengyan Wang
  17. Jinlin Li
  18. Hua Yang
  19. Huihua Xiang
  20. Xiaodan Li
  21. Tao Liu
  22. Ming-Hua Zheng
  23. Chuan Liu
  24. Yifei Huang
  25. Dan Xu
  26. Xiaoguo Li
  27. Ning Kang
  28. Qing He
  29. Ye Gu
  30. Guo Zhang
  31. Chuxiao Shao
  32. Dengxiang Liu
  33. Lin Zhang
  34. Xun Li
  35. Norifumi Kawada
  36. Zicheng Jiang
  37. Fengmei Wang
  38. Bin Xiong
  39. Tetsuo Takehara
  40. Don C Rockey

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  1. ScreenIT

    SciScore for 10.1101/2020.04.24.20072611: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIACUC: The study was approved by the Institutional Ethics Commission of The First Hospital of Lanzhou University (No. LDYYLL2020-37).
    Consent: Informed consent was waived, and researchers analyzed only deidentified data.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variable11 Pregnant women and children (age < 18 years) were excluded.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data were analyzed by SPSS version 20·0 for Windows (SPSS Inc., Chicago, IL, USA).
    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:
    We recognize limitations of the current study. First, the sample size is small, which limits analysis of predictive factors for mortality and the generalizability of the results. Further studies with large sample size and prospective design are needed to better describe the clinical course of COVID-19 in patients with pre-existing cirrhosis and to determine the risk factors for poor outcomes in this setting. Secondly, the majority of patients in this study had underlying hepatitis B virus-related cirrhosis. However, to date there is no evidence to suggest that patients with stable chronic liver disease due to viral hepatitis have increased susceptibility to SARS-CoV-2 infection. In the absence of a liver biopsy, we do not know whether SARS-CoV-2 infection had effects on liver histopathology in our study population. Lastly, the study included only patients from Chinese medical institutions, and whether the data are generalizable to patents in other parts of the world remains unknown. In conclusion, we have reported the demographic characteristics, coexisting conditions, laboratory and imaging findings, and outcomes among COVID-19 patients with pre-existing cirrhosis. It appears that the cause of death in most patients is not due to progressive liver disease (i.e. development of ACLF), but rather pulmonary disease. At presentation, lower lymphocyte and platelet counts, and higher direct bilirubin level may be associated with higher risk of death.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04329559RecruitingCOVID-19 in Patients With Pre-existing Cirrhosis (COVID-Cirr…

    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.

  2. Version published to 10.1136/gutjnl-2020-321666
  3. ScreenIT

    SciScore for 10.1101/2020.04.24.20072611: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementThe study was approved by the Institutional Ethics Commission of The First Hospital of Lanzhou University (No. LDYYLL2020-37).Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variable11 Pregnant women and children (age < 18 years) were excluded.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The data were analyzed by SPSS version 20·0 for Windows (SPSS Inc., Chicago, IL, USA).
    SPSS
    suggested: (SPSS, SCR_002865)

    Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).

    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, please follow this link.

  4. Version published to 10.1101/2020.04.24.20072611v1 on medRxiv