Cancer is associated with the severity and mortality of patients with COVID-19: a systematic review and meta-analysis

  1. Ya Gao
  2. Ming Liu
  3. Shuzhen Shi
  4. Yamin Chen
  5. Yue Sun
  6. Ji Chen
  7. Jinhui Tian

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Abstract

Background

Cancer patients are considered a highly vulnerable population in the COVID-19 epidemic, but the relationship between cancer and the severity and mortality of patients with COVID-19 remains unclear. This study aimed to explore the prevalence of cancer in patients with COVID-19 and to examine whether cancer patients with COVID-19 may be at an increased risk of severe illness and mortality.

Methods

A comprehensive electronic search in seven databases was performed, to identified studies reporting the prevalence of cancer in COVID-19 patients, or providing data of cancer between patients with severe or non-severe illness or between non-survivors and survivors. Meta-analyses were performed to estimate the pooled prevalence and odds risk (OR) using the inverse variance method with the random-effects model.

Results

Thirty-four studies with 8080 patients were included. The pooled prevalence of cancer in patients with COVID-19 was 2.0% (95% CI: 2.0% to 3.0%). The prevalence in Italy (5.0%), France (6.0%), and Korea (4.0%) were higher than that in China (2.0%). Cancer was associated with a 2.84-fold significantly increased risk of severe illness (OR = 2.84, 95%CI: 1.75 to 4.62, P < 0.001) and a 2.60-fold increased risk of death (OR = 2.60, 95%CI: 1.28 to 5.26, P = 0.008) in patients with COVID-19. Sensitivity analyses showed that the results were stable after excluding studies with a sample size of less than 100.

Conclusions

Cancer patients have an increased risk of COVID-19 and cancer was associated with a significantly increased risk of severity and mortality of patients with COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.01.20087031: (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
    The search strategy of PubMed is presented in Appendix Word 1.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    Data extraction and quality assessment: A standardized data extraction form was developed using Microsoft Excel 2016
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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:
    Strengths and limitations: To the best of our knowledge, this is the first meta-analysis systematically evaluated the prevalence of cancer among COVID-19 patients, and the association between cancer and the severity and mortality of patients with COVID-19. Besides, we also conducted sensitivity analyses and meta-regression analyses to evaluate factors that may affect the results. However, our study also has some limitations. First, most of the studies included were from China, so the current findings may not fully reflect the global situation and should be interpreted with caution. Second, although this meta-analysis included 34 studies, there are few data available for subgroup analysis. Third, we performed sensitivity and meta-regression analyses to explore heterogeneity, but some factors were not evaluated due to limited data. Fourth, the patient overlap is still possible between a few studies, although we have ruled out many studies with overlap samples during the study selection and data extraction processes. Finally, we did not evaluate which types of cancer patients are more susceptible to COVID-19 or more associated with severe illness and mortality. As data from more countries become available, it is necessary to update this study and performed more comprehensive analyses to answer questions to guide clinical practice.

    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.

  2. Version published to 10.1101/2020.05.01.20087031v1 on medRxiv