Diabetes is associated with increased risk for in-hospital mortality in patients with COVID-19: a systematic review and meta-analysis comprising 18,506 patients

  1. Leonidas Palaiodimos
  2. Natalia Chamorro-Pareja
  3. Dimitrios Karamanis
  4. Weijia Li
  5. Phaedon D. Zavras
  6. Kai Ming Chang
  7. Priyanka Mathias
  8. Damianos G. Kokkinidis

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Abstract

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  1. Version published to 10.1007/s42000-020-00246-2
  2. ScreenIT

    SciScore for 10.1101/2020.05.26.20113811: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    BlindingData extraction and outcomes: Data extraction was performed based on a pre-defined data extraction form by two independent investigators (NCP, WL) blinded to each other.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Literature search: We conducted a systematic literature search of the Medline, Embase, Google Scholar, and medRxiv (the preprint server for health sciences) up to May 10, 2020 for observational studies providing any kind of association between diabetes and mortality in hospitalized patients with COVID-19.
    Medline
    suggested: (MEDLINE, RRID:SCR_002185)
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    Google Scholar
    suggested: (Google Scholar, RRID:SCR_008878)
    Risk of bias assessment: Two independent reviewers (NCP, PM) assessed the risk of bias of the included studies with Quality in Prognosis Studies tool (QUIPS) (10).
    Quality in Prognosis
    suggested: None

    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:
    The main limitation of our study is the lack of data on glucose control prior or during hospitalization. Therefore, we could not estimate the associations of controlled and uncontrolled diabetes with in-hospital mortality and we recognize that the association would probably be stronger in patients with uncontrolled diabetes and weaker in patients with controlled diabetes. A patient-level meta-analysis would be needed to assess this very important parameter. Second, the estimated association is not adjusted for other important covariates. Unfortunately, only three of the included studies provided adjusted estimates. We tried to solve this methodological issue by performing a meta-regression analysis which showed that the different rates of the other major comorbidities across different studied did not have an impact on the results. Similarly, a patient-level meta-analysis would be the ideal way to adjust for other significant covariates. Third, our meta-analysis was limited by significant heterogeneity, which we tried to assess by using a random-effects model, performing subgroups and sensitivity, and meta-regression analysis. In conclusion, the present systematic review and meta-analysis revealed that the likelihood of death is 65% higher in diabetic hospitalized patients with COVID-19 compared to non-diabetics. Further studies are needed to assess whether this association is independent or not, as well as to investigate to role of glucose control prior or during the disease....

    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 10.1101/2020.05.26.20113811v1 on medRxiv