Adverse outcomes in COVID-19 and diabetes: a retrospective cohort study from three London teaching hospitals

  1. Chioma Izzi-Engbeaya
  2. Walter Distaso
  3. Anjali Amin
  4. Wei Yang
  5. Oluwagbemiga Idowu
  6. Julia S Kenkre
  7. Ronak J Shah
  8. Evelina Woin
  9. Christine Shi
  10. Nael Alavi
  11. Hala Bedri
  12. Niamh Brady
  13. Sophie Blackburn
  14. Martina Leczycka
  15. Sanya Patel
  16. Elizaveta Sokol
  17. Edward Toke-Bjolgerud
  18. Ambreen Qayum
  19. Mariana Abdel-Malek
  20. David C D Hope
  21. Nick S Oliver
  22. Vasiliki Bravis
  23. Shivani Misra
  24. Tricia M Tan
  25. Neil E Hill
  26. Victoria Salem

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Abstract

Patients with diabetes mellitus admitted to hospital with COVID-19 have poorer outcomes. However, the drivers of poorer outcomes are not fully elucidated. We performed detailed characterization of patients with COVID-19 to determine the clinical and biochemical factors that may be drivers of poorer outcomes.

Research design and methods

This is a retrospective cohort study of 889 consecutive inpatients diagnosed with COVID-19 between March 9 and April 22, 2020 in a large London National Health Service Trust. Unbiased multivariate logistic regression analysis was performed to determine variables that were independently and significantly associated with increased risk of death and/or intensive care unit (ICU) admission within 30 days of COVID-19 diagnosis.

Results

62% of patients in our cohort were of non-white ethnic background and the prevalence of diabetes was 38%. 323 (36%) patients met the primary outcome of death/admission to the ICU within 30 days of COVID-19 diagnosis. Male gender, lower platelet count, advancing age and higher Clinical Frailty Scale (CFS) score (but not diabetes) independently predicted poor outcomes on multivariate analysis. Antiplatelet medication was associated with a lower risk of death/ICU admission. Factors that were significantly and independently associated with poorer outcomes in patients with diabetes were coexisting ischemic heart disease, increasing age and lower platelet count.

Conclusions

In this large study of a diverse patient population, comorbidity (ie, diabetes with ischemic heart disease; increasing CFS score in older patients) was a major determinant of poor outcomes with COVID-19. Antiplatelet medication should be evaluated in randomized clinical trials among high-risk patient groups.

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  1. Version published to 10.1136/bmjdrc-2020-001858
  2. ScreenIT

    SciScore for 10.1101/2020.08.07.20160275: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analyses (VS and WD) were performed on GraphPad Prism V8.1 (descriptive statistics) and custom written MaTLAB scripts (multivariate analyses).
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)
    MaTLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2020.08.07.20160275v1 on medRxiv