Clinical Characteristics and Severity of COVID-19 Disease in Patients from Boston Area Hospitals

  1. Hesam Dashti
  2. David W. Bates
  3. Elise Roche
  4. Julie Fiskio
  5. Samia Mora
  6. Olga V. Demler

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Abstract

We summarize key demographic, clinical, and medical characteristics of patients with respect to the severity of COVID-19 disease using Electronic Health Records Data of 4,140 SARS-CoV-2 positive subjects from several large Boston Area Hospitals. We found that prior use of antihypertensive medications as well as lipid lowering and other cardiovascular drugs (such as direct oral anticoagulants and antiplatelets) all track with increased severity of COVID-19 and should be further investigated with appropriate adjustment for confounders such as age and frailty. The three most common prior comorbidities are hyperlipidemia, hypertension, and prior pneumonia, all associated with increased severity.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.27.20163071: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the Partners Institutional Review Board.
    Consent: Obtaining consent from all individuals would make the study challenging logistically, financially and scientifically, especially due to the urgent nature of the pandemic.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Python and R software languages were used.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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.
    • 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.

  2. Version published to 10.1101/2020.07.27.20163071v1 on medRxiv