Factors Affecting COVID-19 Outcomes in Cancer Patients: A First Report From Guy's Cancer Center in London

  1. Beth Russell
  2. Charlotte Moss
  3. Sophie Papa
  4. Sheeba Irshad
  5. Paul Ross
  6. James Spicer
  7. Shahram Kordasti
  8. Danielle Crawley
  9. Harriet Wylie
  10. Fidelma Cahill
  11. Anna Haire
  12. Kamarul Zaki
  13. Fareen Rahman
  14. Ailsa Sita-Lumsden
  15. Debra Josephs
  16. Deborah Enting
  17. Mary Lei
  18. Sharmistha Ghosh
  19. Claire Harrison
  20. Angela Swampillai
  21. Elinor Sawyer
  22. Andrea D'Souza
  23. Simon Gomberg
  24. Paul Fields
  25. David Wrench
  26. Kavita Raj
  27. Mary Gleeson
  28. Kate Bailey
  29. Richard Dillon
  30. Matthew Streetly
  31. Anne Rigg
  32. Richard Sullivan
  33. Saoirse Dolly
  34. Mieke Van Hemelrijck

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Abstract

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  1. Version published to 10.3389/fonc.2020.01279
  2. ScreenIT

    SciScore for 10.1101/2020.05.12.20094219: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study population: Guy’s Cancer Cohort, a research ethics committee approved research database (Reference number: 18/NW/0297) of all routinely collected clinical data of cancer patients at Guy’s and St Thomas’ NHS Foundation Trust (GSTT), forms the basis of this observational study (25).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We assessed outcomes included in the core outcome sets currently being developed for COVID-19 to ensure all relevant information is collected in our COVID specific database (26).
    COVID
    suggested: (CovidNLP, RRID:SCR_018513)
    All statistical analyses were conducted with STATA version 15.1
    STATA
    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:
    However, longer follow-up studies are required to disentangle the association between ethnicity and COVID-19 death in cancer patients Strengths and limitations: Whilst this is one of the largest single centre COVID-19 positive cancer cohorts to date, our sample size is still relatively modest and hence confidence intervals for some statistically significant observations are still wide. No firm conclusions in terms of prognostic modelling can be drawn as of yet (19). Current analyses were aimed at hypothesis generation about patient or tumour characteristics indicative of severity of or death from COVID-19 in the cancer context. Our data for some of the patient characteristics is limited, for example smoking status was missing for 29% of patients and hence likely underestimates the proportion of ever smokers. COVID testing in the UK has only been implemented gradually during the period of our data collection, and there is selection bias in favour of patients being tested as inpatients. Our analysis is likely to have missed cancer outpatients under our care diagnosed with COVID-19 at other hospitals – however this is most likely to be an even more important issue for global Consortia with many hospitals only adding a few cases to the overall dataset. It is a strength of our study that we used clearly defined definitions of COVID-19 severity, as well as a DAG to develop the different models, as to date very limited knowledge is available regarding the intersection between COVID-...

    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 10.1101/2020.05.12.20094219v2 on medRxiv
  4. Version published to 10.1101/2020.05.12.20094219v1 on medRxiv