Assessment of correlations between risk factors and symptom presentation among defined at-risk groups following a confirmed COVID-19 diagnosis

  1. Dylan Aidlen
  2. Jamie Henzy

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Abstract

This study analyzes the specific linkages between symptoms within individual COVID patients belonging to at-risk groups. The goal was to determine how strongly linked patient symptoms are within these at-risk groups to find any associations between factors such as comorbidities and COVID symptoms. In this study, de-identified patient data from the N3C database was utilized in order to link representative immunocompromised states with specific symptoms, and non-immunocompromised state with the same, to determine if the strength of the correlation changes for these at-risk groups. Multiple autoimmune disorders resulting in immunocompromised state were analyzed, to determine if severity of immune response and inflammatory action plays a role in any potential differences. An exploratory approach using statistical methods and visualization techniques appropriate to multidimensional data sets was taken. The identified correlations may allow pattern analysis in disease presentation specific to a given population, potentially informing pattern recognition, symptom presentation, and treatment approaches in patients with immune comorbidities.

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  1. ScreenIT

    SciScore for 10.1101/2021.11.30.21267029: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    For each of the chosen symptoms, we found all records, codes, IDs, and domains relating them across all classification vocabularies (SNOMED, RxNorm, Nebraska Lexicon, etc).
    RxNorm
    suggested: (RxNorm, RRID:SCR_006645)

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.11.30.21267029v1 on medRxiv