Epidemiological and clinical characteristics of cancer patients with COVID-19: A systematic review and meta-analysis of global data

  1. Xiangyi Kong
  2. Yihang Qi
  3. Junjie Huang
  4. Yang Zhao
  5. Yongle Zhan
  6. Xuzhen Qin
  7. Zhihong Qi
  8. Adejare (Jay) Atanda
  9. Lei Zhang
  10. Jing Wang
  11. Yi Fang
  12. Peng Jia
  13. Asieh Golozar
  14. Lin Zhang
  15. Yu Jiang

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Abstract

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  1. Version published to 10.1016/j.canlet.2021.02.012
  2. ScreenIT

    SciScore for 10.1101/2020.08.20.20177311: (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 variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    To identify studies on COVID-19 in cancer patients, we searched PubMed, Embase, Cochrane Library and Web of Science from the inception of each database to June 31, 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Cochrane Library
    suggested: (Cochrane Library, RRID:SCR_013000)
    Key/relevant MeSh terms and keywords included the following keywords: “2019-ncov”, “novel coronavirus”, “COVID-19”, “SARS-CoV-2”, “new coronavirus”, “coronavirus disease 2019”, “cancer”, “tumor”, “malignancy”, and “neoplasm”, etc.
    MeSh
    suggested: (MeSH, RRID:SCR_004750)
    All analyses were performed with Stata version 14.2 (StataCorp, College Station, TX, USA) [
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    In our study, because the data of potential risk factors associated with mortality was limited among the included studies, we did not assess the association between the clinical outcome and potential prognostic variables mentioned above, a limitation of this study. However, one of the novelties and strengths of the present meta-analysis is that besides the prevalence, we also performed the subgroup analysis as a function of continent for incidence of severe illness and death rate among COVID-19 patients with cancers. There were some interesting findings (Figure 11): 1) the European COVID-19 patients had both the highest cancer prevalence (0.22) and cancer patient mortality (0.26); 2) the North American COVID-19 patients had a similar cancer prevalence as Asia Pacific patients, but had the lowest cancer patient severe illness rate (0.26); 3) compared with Asia Pacific, the European COVID-19 patients had a much higher cancer prevalence, but their cancer patient severe illness rates were similar (Asia Pacific 0.35 vs. Europe 0.36); and 4) compared with Asia Pacific, the North American COVID-19 patients had a similar cancer prevalence and cancer patient mortality, but the incidence of severe illness of cancer patients (0.26) was much lower. Overall, the European COVID-19 patients seemed the most likely to both develop cancer and progress to severe illness and death (for COVID-19 patients with cancers). Although the Asia Pacific COVID-19 patients had the lowest cancer prevalence, ...

    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 10.1101/2020.08.20.20177311v1 on medRxiv