Clinical Outcome of Asymptomatic COVID-19 Infection Among a Large Nationwide Cohort of 5,621 Hospitalized Patients in Korea

  1. Hayne Cho Park
  2. Do Hyoung Kim
  3. Ajin Cho
  4. Juhee Kim
  5. Kyu-sang Yun
  6. Jinseog Kim
  7. Young-Ki Lee

Reviewed by

Read the full article

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

We investigated clinical outcome of asymptomatic coronavirus disease 2019 (COVID-19) and identified risk factors associated with high patient mortality using Korean nationwide public database of 5,621 hospitalized patients. The mortality rate and admission rate to intensive care unit were compared between asymptomatic and symptomatic patients. The prediction model for patient mortality was developed through risk factor analysis among asymptomatic patients. The prevalence of asymptomatic COVID-19 infection was 25.8%. The mortality rates were not different between groups (3.3% vs. 4.5%, p=0.17). However, symptomatic patients were more likely to receive ICU care compared to asymptomatic patients (4.1% vs. 1.0%, p<0.0001). The age-adjusted Charlson comorbidity index score (CCIS) was the most potent predictor for patient mortality in asymptomatic patients. The clinicians should predict the risk of death by evaluating age and comorbidities but not the presence of symptoms.

Article Summary Line

Since asymptomatic patients have similar mortality rate with symptomatic patients, the clinicians should not classify clinical severity according to initial presence of symptom.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.10.25.20218982: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The present study protocol was reviewed and approved by the Institutional Review Board of the Kangnam Sacred Heart Hospital, Seoul, Korea (HKS 2020-06-025).
    Consent: The informed consent was waived due to retrospective nature of the study.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analysis was performed by using R version 4.0.2 (R Foundation for Statistical Computing; http://www.r-project.org/).
    http://www.r-project.org/
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    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:
    There are some limitations to our study. First, our study excluded those who admitted to the community treatment centers and only included the hospitalized patients. Therefore, there can be a selection bias to generalize our results. Second, we defined asymptomatic patients as those who presented without symptoms at admission. Therefore, some of them would have been developed symptoms at some time after admission. The previous study by Korean researchers demonstrated that about one-third of initially asymptomatic patients developed symptoms during clinical course (8). Therefore, we cannot conclude from our study that the prognosis of asymptomatic patients during entire clinical course is similar to that of those who developed symptoms afterwards. Lastly, the patients with dementia and chronic obstructive pulmonary disease or asthma may have under-reported their symptoms, which may result in overestimation of asymptomatic patients. In conclusion, we found that asymptomatic patients have same mortality risk with symptomatic patients with COVID-19. Our study suggest that asymptomatic patients should not be considered ‘less severe’ than symptomatic patients in treating COVID-19. Regardless of the presence of symptoms, we should predict the clinical risks of the patients based upon age and comorbidities and treat them accordingly.

    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 10.1101/2020.10.25.20218982 on medRxiv