Differences in Outcomes and Factors Associated With Mortality Among Patients With SARS-CoV-2 Infection and Cancer Compared With Those Without Cancer

  1. Emma Khoury
  2. Sarah Nevitt
  3. William Rohde Madsen
  4. Lance Turtle
  5. Gerry Davies
  6. Carlo Palmieri

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Abstract

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  1. Version published to 10.1001/jamanetworkopen.2022.10880
  2. ScreenIT

    SciScore for 10.1101/2021.12.18.21267261: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variableData extraction and quality assessment: Two authors (E.K. and C.P.) independently undertook data extraction for the following: first author, study type, time period of data collection, country of data collection, number of male and female patients, median or mean age, cancer treatment intervals prior to COVID-19 diagnosis or hospitalisation, unadjusted and adjusted odds ratios (ORs) or hazard ratios (HRs) for severe disease and death for each cancer subtype and for each cancer treatment subtype as well as the number of cancer patients with COVID-19 and cancer type.
    RandomizationTo examine the impact of age and sex on mortality among cancer patients and controls, random-effects meta-regressions were conducted.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Repeated searches of PubMed, Web of Science and Scopus databases were performed up to 14th June 2021.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)

    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:
    This systematic review demonstrates a number of potential limitations with the currently available data and literature these include; (1) lack of contemporaneous non-cancer populations for comparative analysis 12,19,23,36,38,40; (2) heterogeneity of definitions between studies such as severity of COVID-19 as demonstrated by the 14 definitions of severity used across studies (Figure S13;) (3) predominantly retrospective nature of studies (Table S4, 75%); (4) variable follow up; (5) heterogeneity or poor description of the non-cancer control cohorts, such as use of non-hospitalised patients 12,21,29,36–38,44, or staff members with COVID-19 43; and (6) lack of detail relating to systemic cancer treatment 4,11,12,22,29,33,37,39,41,42,50,52,55–57,65,70–72,75,76,79–81,84,86–91. In those studies where a non-cancer cohort was utilised these data were generally historical or based on registry data and were not contemporaneous to the cancer cohort 12,19,36,38,43. Only three of the 19 studies that compared cancer cohort to a matched non-cancer cohort used propensity score matching 14,16,39. Therefore, within the published literature biases may be present as a result of unmeasured confounders. Utilising data from 19 studies with 3,926 patients with 38,847 non-contemporaneous non-cancer patients, we found that malignant disease is associated with an increased risk of severe or death from COVID-19 compared with non-cancer patients (RR 2.12; 95% CI, 1.76-2.62; p<0.001; I2=84.4%). In particu...

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.12.18.21267261v1 on medRxiv