The impact of coronavirus disease 2019 (COVID-19) on liver injury in China

  1. Xin Zhao
  2. Zehua Lei
  3. Fengwei Gao
  4. Qingyun Xie
  5. Kangyi Jang
  6. Jie Gong

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Abstract

Introduction:

The evidence for the incidence and severity of liver injury in Chinese patients with coronavirus disease 2019 (COVID-19) is still controversial. The purpose of this study was to summarize the incidence of liver injury and the differences between liver injury markers among different patients with COVID-19 in China.

Methods:

Computer searches of PubMed, Embase, China National Knowledge Infrastructure (CNKI) and medRxiv were used to obtain reports on the incidence and markers of liver injury in Chinese patients with COVID-19, from January 1, 2020 to April 10, 2020. (No. CRD42020181350)

Results:

A total of 57 reports from China were included, including 9889 confirmed cases of COVID-19 infection. The results of the meta-analysis showed that among the patients with early COVID-19 infection in China, the incidence of liver injury events was 24.7% (95% CI, 23.4%–26.4%). Liver injury in severe patients was more common than that in non-severe patients, with a risk ratio of 2.07 (95% CI, 1.77–2.43). Quantitative analysis showed that the severe the coronavirus infection, the higher the level of alanine aminotransferase (ALT), aspertate aminotransferase (AST), total bilirubin (TB), alkaline phosphatase (ALP), γ-glutamyl transpeptidase (GGT), and the lower the level of albumin (ALB).

Conclusion:

There is a certain risk of liver injury in Chinese patients with COVID-19, and the risk and degree of liver injury are related to the severity of COVID-19.

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  1. Version published to 10.1097/md.0000000000024369
  2. ScreenIT

    SciScore for 10.1101/2020.05.03.20089557: (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 variableThe following studies were excluded: (1) COVID-19 patients without nucleic acid diagnosis or clinical diagnosis; (2) reports of special groups such as pregnant women and children; (3) studies that only report deaths or critically ill patients; (4) studies that do not report liver injury events or markers of liver injury; (5) research reports that the participants are not from China.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search strategy: Three electronic databases, PubMed, Embase, CNKI and the medRxiv system (https://www.medrxiv.org), were searched by our team.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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:
    Owing to data limitations, we did not further analyse whether liver injury increases the risk of death in patients with COVID-19. Our study also has some limitations. First, although there is no obvious bias in the definition of liver injury and detection of liver injury markers, some of the results are still heterogeneous. Second, this study only analysed the data of Chinese COVID-19 patients, not remote data analysis. Third, it is not possible to further describe the effects of other confounding factors, such as complications, age, and gender, on the results of the study; these confounding factors are also difficult to adjust. Fourth, most of the included studies are retrospective analyses, some are cross-sectional studies, 22 are manuscripts that have not been peer-reviewed, and there is a danger of bias in data collection.

    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 10.1101/2020.05.03.20089557v1 on medRxiv