Systematic investigations of COVID-19 in 283 cancer patients

  1. Jie Wang
  2. Qibin Song
  3. Yuan Chen
  4. Zhijie Wang
  5. Qian Chu
  6. Hongyun Gong
  7. Shangli Cai
  8. Xiaorong Dong
  9. Bin Xu
  10. Weidong Hu
  11. Qun Wang
  12. Linjun Li
  13. Jiyuan Yang
  14. Zhibin Xie
  15. Zhiguo Luo
  16. Jing Liu
  17. Xiuli Luo
  18. Jie Ren
  19. Zhiguo Rao
  20. Xinhua Xu
  21. Dongfeng Pan
  22. Zuowei Hu
  23. Gang Feng
  24. Chiding Hu
  25. Liqiong Luo
  26. Hongda Lu
  27. Ruizhi Ran
  28. Jun Jin
  29. Yanhua Xu
  30. Yong Yang
  31. Zhihong Zhang
  32. Li Kuang
  33. Runkun Wang
  34. Youhong Dong
  35. Jianhai Sun
  36. Wenbing Hu
  37. Tienan Yi
  38. Hanlin Wu
  39. Mingyu Liu
  40. Jiachen Xu
  41. Jianchun Duan
  42. Zhengyi Zhao
  43. Guoqiang Wang
  44. Yu Xu
  45. Jie He

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Abstract

Background

Cancer patients are considered to be highly susceptible to viral infections, however, the comprehensive features of COVID-19 in these patients remained largely unknown. The present study aimed to assess the clinical characteristics and outcomes of COVID-19 in a large cohort of cancer patients.

Design, Setting, and Participants

Data of consecutive cancer patients admitted to 33 designated hospitals for COVID-19 in Hubei province, China from December 17, 2019 to March 18, 2020 were retrospectively collected. The follow-up cutoff date was April 02, 2020. The clinical course and survival status of the cancer patients with COVID-19 were measured, and the potential risk factors of severe events and death were assessed through univariable and multivariable analyses.

Results

A total of 283 laboratory confirmed COVID-19 patients (50% male; median age, 63.0 years [IQR, 55.0 to 70.0]) with more than 20 cancer types were included. The overall mortality rate was 18% (50/283), and the median hospitalization stay for the survivors was 26 days. Amongst all, 76 (27%) were former cancer patients with curative resections for over five years without recurrence. The current cancer patients exhibited worse outcomes versus former cancer patients (overall survival, HR=2.45, 95%CI 1.10 to 5.44, log-rank p =0.02; mortality rate, 21% vs 9%). Of the 207 current cancer patients, 95 (46%) have received recent anti-tumor treatment, and the highest mortality rate was observed in the patients receiving recent chemotherapy (33%), followed by surgery (26%), other anti-tumor treatments (19%), and no anti-tumor treatment (15%). In addition, a higher mortality rate was observed in patients with lymphohematopoietic malignancies (LHM) (53%, 9/17), and all seven LHM patients with recent chemotherapy died. Multivariable analysis indicated that LHM ( p =0.001) was one of the independent factors associating with critical illness or death.

Conclusions

This is the first systematic study comprehensively depicting COVID-19 in a large cancer cohort. Patients with tumors, especially LHM, may have poorer prognosis of COVID-19. Additional cares are warranted and non-emergency anti-tumor treatment should be cautiously used for these patients under the pandemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.28.20083246: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: All inconsistencies were reviewed and confirmed by the Medical Review Board composed of physicians from departments of oncology, thoracic surgery, and pulmonary and critical care medicine.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The analyses were performed with the use of R software (version 3.5.0, R Foundation for Statistical Computing) and GraphPad Prism software (version 5.0, GraphPad Software Inc.).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    GraphPad
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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:
    There are several limitations in this study. Firstly, this study was conducted in a retrospective setting. Nevertheless, the patient population in this study was relatively large, and the patients were consecutively included irrespective of their baseline characteristics including the clinical symptoms, which will reduce the potential bias of the study population. Secondly, this study focused on the investigation of patients with cancer infected with SARS-CoV-2, and no direct comparison was conducted between the infected cancer patients and the non-cancer patients. Despite that, comparisons have been made between the current and the former cancer patients, who were considered to exhibit relatively more likely to cancer-free patients as a reference. Besides, the studied population came from as many as 33 designated hospitals in Hubei province, China, which is considered to be fairly representative for the overall condition of COVID-19 cancer patients in Hubei province, and thus was compared with the data of general COVID-19 patients in Hubei province over the same period.

    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 10.1101/2020.04.28.20083246v1 on medRxiv