Thromboembolism risk among patients with diabetes/stress hyperglycemia and COVID-19

  1. Stefania L. Calvisi
  2. Giuseppe A. Ramirez
  3. Marina Scavini
  4. Valentina Da Prat
  5. Giuseppe Di Lucca
  6. Andrea Laurenzi
  7. Gabriele Gallina
  8. Ludovica Cavallo
  9. Giorgia Borio
  10. Federica Farolfi
  11. Maria Pascali
  12. Jacopo Castellani
  13. Vito Lampasona
  14. Armando D'Angelo
  15. Giovanni Landoni
  16. Fabio Ciceri
  17. Patrizia Rovere Querini
  18. Moreno Tresoldi
  19. Lorenzo Piemonti

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Abstract

No abstract available

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  1. Version published to 10.1016/j.metabol.2021.154845
  2. Version published to 10.1101/2021.04.17.21255540v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.04.17.21255540: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study was approved by the local Institutional Review Board (protocol n 34/int/2020; NCT04318366).
    Consent: Patients signed a written informed consent granting permission to access their sensitive data for the purposes of this study.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    BlindingData were cross-checked in blind and verified by data managers and clinicians for accuracy.
    Power Analysisnot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    Antiphospholipid antibodies including IgG and IgM anticardiolipin (aCL), antiβ2-glycoprotein (aB2GPI) and anti-phosphatidylserine/prothrombin (aPSPT) antibodies were measured by using a Bioflash® automated chemiluminescent analyser.
    Antiphospholipid antibodies including IgG and IgM anticardiolipin ( aCL)
    suggested: None
    antiβ2-glycoprotein
    suggested: None
    anti-phosphatidylserine/prothrombin ( aPSPT )
    suggested: None
    Software and Algorithms
    SentencesResources
    Statistical analyses were performed with the SPSS 24 (SPSS Inc. /IBM) and the R software version 3.4.0
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our study encompasses some limitations: first, our cohort was limited to hospitalized patients and results could be different in less severe COVID-19 disease. Second, the definition of newly diagnosed diabetes did not exclude stress-induced hyperglycemia and, as the study included mainly patients with the characteristics of type 2 diabetes, we cannot generalize our findings to other types of diabetes. Third, even if the overall venous and arterial thromboembolism rate was similar to that described until now in various studies (18), our monocentric cohort was relatively small, and, therefore, a selection bias cannot be excluded. Fourth, we were unable to evaluate the specific role of some markers as predictors of thrombosis in multivariate models since a complete set of biochemical coagulation data was available only for a fraction of patients. Nevertheless our study generated additional valuable knowledge about the role of diabetes in predicting thrombotic events and in stratifying their prognostic significance. In conclusion, many evidences indicate that patients with diabetes, in case of COVID-19 pneumonia, carry a significant increased risk for adverse clinical outcome when compared with patients without diabetes. It is clear from our study that part of this risk is due to an increase in thromboembolic complications. These findings suggest that in in case of SARS-Cov-2 pneumonia, patients with diabetes could be considered for a more intensive prophylactic anticoagulation r...

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

    IdentifierStatusTitle
    NCT04318366RecruitingCOVID-19 Patients Characterization, Biobank, Treatment Respo…

    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.

  4. Version published to 10.1101/2021.04.17.21255540v1 on medRxiv