Prevalence and risks of severe events for cancer patients with COVID-19 infection: a systematic review and meta-analysis

  1. Qiang Su
  2. Jie-xuan Hu
  3. Hai-shan Lin
  4. Zheng Zhang
  5. Emily C. Zhu
  6. Chen-guang Zhang
  7. Di-ya Wang
  8. Zu-hua Gao
  9. Bang-wei Cao

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

The corona virus disease 2019 (COVID-19) pandemic poses a severe challenge to public health, especially to those patients with underlying diseases. In this meta-analysis, we studied the prevalence of cancer among patients with COVID-19 infection and their risks of severe events.

Methods

We searched the Pubmed, Embase and MedRxiv databases for studies between December 2019 and May 3, 2020 using the following key words and terms: sars-cov-2, covid-19, 2019-ncov, 2019 novel coronavirus, corona virus disease-2019, clinical, clinical characteristics, clinical course, epidemiologic features, epidemiology, and epidemiological characteristics. We extracted data following PICO (patient, intervention, comparison and outcome) chart. Statistical analyses were performed with R Studio (version 3.5.1) on the group-level data. We assessed the studies’ risk of bias in accordance to the adjusted Joanna Briggs Institute. We estimated the prevalence or risks for severe events including admission into intensive care unit or death using meta-analysis with random effects.

Findings

Out of the 2,551 studies identified, 32 studies comprising 21,248 participants have confirmed COVID-19. The total prevalence of cancer in COVID-19 patients was 3.97% (95% CI, 3.08% to 5.12%), higher than that of the total cancer rate (0.29%) in China. Stratification analysis showed that the overall cancer prevalence of COVID-19 patients in China was 2.59% (95% CI, 1.72% to 3.90%), and the prevalence reached 3.79% in Wuhan (95% CI, 2.51% to 5.70%) and 2.31% (95% CI, 1.16% to 4.57%) in other areas outside Wuhan in China. The incidence of ICU admission in cancer patients with COVID-19 was 26.80% (95% CI, 21.65% to 32.67%) and the mortality was 24.32% (95% CI, 13.95% to 38.91%), much higher than the overall rates of COVID-19 patients in China. The fatality in COVID patients with cancer was lower than those with cardiovascular disease (OR 0.49; 95% CI, 0.34 to 0.71; p=0.39), but comparable with other comorbidities such as diabetes (OR 1.32; 95% CI, 0.42 to 4.11; p=0.19), hypertension (OR 1.27; 95% CI, 0.35 to 4.62; p=0.13), and respiratory diseases (OR 0.79; 95% CI, 0.47 to 1.33; p=0.45).

Interpretation

This comprehensive meta-analysis on the largest number of patients to date provides solid evidence that COVID-19 infection significantly and negatively affected the disease course and prognosis of cancer patients. Awareness of this could help guide clinicians and health policy makers in combating cancer in the context of COVID-19 pandemic.

Funding

Beijing Natural Science Foundation Program and Scientific Research Key Program of Beijing Municipal Commission of Education (KZ202010025047).

Article activity feed

  1. Version published to 10.1101/2020.06.23.20136200v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.06.23.20136200: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Search Methods and Study Selection: We searched Pubmed, Embase and MedRxiv databases for studies according to the following key words and terms: sars-cov-2, covid-19, 2019-ncov, 2019 novel coronavirus, Corona Virus Disease-2019, clinical, clinical characteristics, clinical course, epidemiologic features, epidemiology, epidemiological characteristics.
    Pubmed
    suggested: (PubMed, RRID:SCR_004846)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: Our meta-analysis studied the cancer prevalence and risks of severe events in COVID-19 patients by accommodating fully reported and censored data from diverse literature. But this meta-analysis has limitations. Small-study effects were observed when studies with smaller sample sizes had different incidences and wider CIs for fatality in patients with COVID-19 and cancer. Although the forest plots showed no asymmetry favoring cancer fatality studies, this meta-analysis is subject to publication bias given that all of our analyses were based on publications. In addition, this study is subject to any biases or errors of the original investigators, and the results are generalizable only to patient groups with COVID-19. Meanwhile, it currently cannot summarize the prevalence or risk of severe events of different types of cancer in COVID-19 patients due to the lack of enough 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

    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 10.1101/2020.06.23.20136200v1 on medRxiv