A Systematic Review and Meta-Analysis of Cancer Patients Affected by a Novel Coronavirus

  1. Bhanu Prasad Venkatesulu
  2. Viveksandeep Thoguluva Chandrasekar
  3. Prashanth Girdhar
  4. Pragati Advani
  5. Amrish Sharma
  6. Thiraviyam Elumalai
  7. Cheng En Hsieh
  8. Hagar I Elghazawy
  9. Vivek Verma
  10. Sunil Krishnan

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Abstract

Background

Cancer patients with coronavirus disease 2019 (COVID-19) have been reported to have double the case fatality rate of the general population.

Methods

A systematic search of PubMed, Embase, and Cochrane Central was done for studies on cancer patients with COVID-19. Pooled proportions were calculated for categorical variables. Odds ratio (OR) and forest plots (random-effects model) were constructed for both primary and secondary outcomes.

Results

This systematic review of 38 studies and meta-analysis of 181 323 patients from 26 studies included 23 736 cancer patients. Our meta-analysis shows that cancer patients with COVID-19 have a higher likelihood of death (n = 165 980, OR = 2.54, 95% confidence interval [CI] = 1.47 to 4.42), which was largely driven by mortality among patients in China. Cancer patients were more likely to be intubated. Among cancer subtypes, the mortality was highest in hematological malignancies (n = 878, OR = 2.39, 95% CI = 1.17 to 4.87) followed by lung cancer (n = 646, OR = 1.83, 95% CI = 1.00 to 3.37). There was no association between receipt of a particular type of oncologic therapy and mortality. Our study showed that cancer patients affected by COVID-19 are a decade older than the normal population and have a higher proportion of comorbidities. There was insufficient data to assess the association of COVID-19–directed therapy and survival outcomes in cancer patients.

Conclusion

Cancer patients with COVID-19 disease are at increased risk of mortality and morbidity. A more nuanced understanding of the interaction between cancer-directed therapies and COVID-19–directed therapies is needed. This will require uniform prospective recording of data, possibly in multi-institutional registry databases.

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  1. Version published to 10.1093/jncics/pkaa102
  2. ScreenIT

    SciScore for 10.1101/2020.05.27.20115303: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Institutional review board approval was not required for this study since no patient (CRD42020186671).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    A few caveats about our analysis are noteworthy. The pooled mortality rates in such a meta-analysis may be misleading given that cancer patients are often older and have more comorbidities. Hence, the actual magnitude of mortality in cancer patients with COVID-19 using age-matched cohorts might be lower than reported in these studies. Additionally, mortality differences seemed to be driven by Chinese patients, which could imply unforeseen COVID-19-treatment-related effects or genetic polymorphisms as compared to Western populations. Within Western populations, the findings of our analysis may still help inform how resources are redeployed within oncology units or cancer centers. For instance, the greater mortality among patients with hematological malignancies may argue for marshalling additional resources to these units in a cancer center. It may also support a conscious decision to delay highly immunosuppressive treatment such as bone marrow transplantation, stem cell transplantation or CAR-T cell therapy. However, modifying standard therapeutic protocols to accommodate predicted disparities in mortality engenders additional risks in terms of disease progression. Clearly, such modifications should be guided by a nuanced risk-benefit analysis based on the best available data.

    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

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  3. Version published to 10.1101/2020.05.27.20115303v1 on medRxiv