Risk of Cardiovascular Events After COVID-19

  1. Larisa G. Tereshchenko
  2. Adam Bishop
  3. Nora Fisher-Campbell
  4. Jacqueline Levene
  5. Craig C. Morris
  6. Hetal Patel
  7. Erynn Beeson
  8. Jessica A. Blank
  9. JG N. Bradner
  10. Michelle Coblens
  11. Jacob W. Corpron
  12. Jenna M. Davison
  13. Kathleen Denny
  14. Mary S. Earp
  15. Simeon Florea
  16. Howard Freeman
  17. Olivia Fuson
  18. Florian H. Guillot
  19. Kazi T. Haq
  20. Morris Kim
  21. Clinton Kolseth
  22. Olivia Krol
  23. Lisa Lin
  24. Liat Litwin
  25. Aneeq Malik
  26. Evan Mitchell
  27. Aman Mohapatra
  28. Cassandra Mullen
  29. Chad D Nix
  30. Ayodele Oyeyemi
  31. Christine Rutlen
  32. Ashley E. Tam
  33. Inga Van Buren
  34. Jessica Wallace
  35. Akram Khan

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Abstract

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  1. Version published to 10.1016/j.amjcard.2022.06.023
  2. ScreenIT

    SciScore for 10.1101/2021.12.27.21268448: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study has been approved by the OHSU Institutional Review Board (IRB) and was registered (http://www.clinicaltrials.gov.
    Sex as a biological variablenot detected.
    RandomizationThe study investigators drew a simple random sample of records from the pool of all EMRs with available results of the COVID-19 test, performed at OHSU Healthcare between March 1st, 2020, and September 13th, 2020.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The study investigators drew a simple random sample of records from the pool of all EMRs with available results of the COVID-19 test, performed at OHSU Healthcare between March 1st, 2020, and September 13th, 2020.
    OHSU Healthcare
    suggested: None
    7 (StataCorp LP, College Station, TX).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)
    STATA do files are available at https://github.com/Tereshchenkolab/statistics.
    STATA
    suggested: (Stata, RRID:SCR_012763)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: Important limitations of the study need to be considered. As in any retrospective cohort study, investigators had no control over the quality and completeness of the available EMR data. The likelihood of unobserved and unmeasured confounding cannot be eliminated entirely, as an observational study is susceptible to confounding bias. Two cohorts assembled from the different COVDI-19(+) and COVID-19(-) populations may differ in multiple important ways that influenced the outcomes. We cannot completely rule out the violation of the conditional independence assumption. In our observational study, the treatment (SARS-CoV-2 infection exposure) was not randomly assigned so potential outcomes are not independent of the exposure. We assumed that after conditioning on the covariates, the treatment assignment was as good as random. Nevertheless, we cannot be 100% sure that we observed, measured, and conditioned on enough covariates. We also note that the study was conducted in a single healthcare system. Validation of the study findings in alternative populations will increase the chances that the observed association is causal.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04555187Active, not recruitingCardiovascular Risk Stratification in Covid-19

    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 10.1101/2021.12.27.21268448v1 on medRxiv