Hospitalized Coronavirus Disease 2019 (Covid-19) with Pre-existing Diabetes Mellitus: Comparison between Survived and Deceased Patients

  1. Md Shahed Morshed
  2. Abdullah Al Mosabbir
  3. Mohammad Sorowar Hossain

Reviewed by

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Patients with pre-existing diabetes mellitus (DM) are at high risk of severe outcomes from coronavirus disease 2019 (COVID-19). However, there is limited data on these patients from South Asia, especially Bangladesh. Besides, comparative studies between survived and deceased patients with DM and COVID-19 are rare in literature. This retrospective cross-sectional study was conducted among RT-PCR confirmed COVID-19 patients with pre-existing DM in a specialized COVID-19 hospital in Bangladesh. Data from hospital records were analyzed. Among 921 RT-PCR confirmed patients with COVID-19 admitted during the study period, 231 (~25%) patients had pre-existing DM. The overall mortality and intensive care unit (ICU) mortality rate among patients with DM was 11.3% (26/231) and 46.2% (12/26), respectively. The median age of the deceased patients was slightly higher (63.5 vs. 59 years, p 0.21). The most common comorbidity in both groups was hypertension. The clinical features were not significantly different between survived and deceased. However, deceased patients had significantly lower blood oxygen level (85% vs. 93%, p <0.001), and higher neutrophil-lymphocyte ratio (7.9 vs. 4.5, p 0.003) and serum ferritin levels (946.0 vs. 425.0 ng/mL, p 0.03). Glycemic status was poor in both groups. This study would help identify a subgroup of diabetic patients with COVID-19 who are at higher risk of in-hospital death requiring rigorous clinical management. Bangladesh J Microbiol, Volume 39, Number 1, June 2022, pp 1-6

Article activity feed

  1. Version published to 10.3329/bjm.v39i1.64052
  2. ScreenIT

    SciScore for 10.1101/2021.04.04.21254884: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The institutional review board of the Biomedical Research Foundation, Bangladesh, approved the study protocol (Ref. no: BRF/ERB/2020/003).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data were analyzed by SPSS 22.0 software.
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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:
    Our study has some limitations. The sample size was not large enough to evaluate the predictive performance of different parameters for death in diabetic patients. Besides, we could not collect data on the type of DM or new-onset DM.

    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 10.1101/2021.04.04.21254884 on medRxiv