Longitudinal symptom and clinical outcome analysis of hospitalized COVID-19 patients

  1. Arturas Ziemys

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

COVID-19 pandemics increased patient hospitalization impacting the hospital operations and patient care beyond COVID-19 patients. Although longitudinal symptom analysis may provide prognostic utility about clinical outcomes and critical hospitalization events of COVID-19 patients, such analysis is still missing. Here, we have analyzed over 10,000 hospitalized COVID-19 patients in the Houston Methodist Hospital at the Texas Medical Center from the beginning of pandemics till April of 2020. Our study used statistical and regression analysis over symptoms grouped into symptom groups based on their anatomical locations. Symptom intensity analysis indicated that symptoms peaked at the time of admission and subsided within the first week of hospitalization for most of the patients. Patients surviving the infection (n=9,263), had faster remission rates, usually within the first days of hospitalization compared to sustained symptom for the deceased patient group (n=1,042). The latter had also a longer hospitalization stay and more comorbidities including diabetes, cardiovascular, and kidney disease. Inflammation-associated systemic symptoms (Systemic) such as fever and chills, and lower respiratory system specific symptoms (Lower Respiratory System) such as shortness of breath and pneumonia, were the most informative for the analysis of longitudinal symptom dynamics. Our results suggest that the symptom remission rate could possess prognostic utility in evaluating patient hospitalization stay and clinical outcomes early in hospitalization. We believe knowledge and information about symptom remission rates can be used to improve hospital operations and patient care by using common and relatively easy to process source of information.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2022.01.11.22268908: (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
    Data analysis: Data set was analyzed in LibreOffice spreadsheets and Python 3.
    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2022.01.11.22268908v1 on medRxiv