Simple scoring tool to estimate risk of hospitalization and mortality in ambulatory and emergency department patients with COVID-19

  1. Brandon J. Webb
  2. Nicholas M. Levin
  3. Nancy Grisel
  4. Samuel M. Brown
  5. Ithan D. Peltan
  6. Emily S. Spivak
  7. Mark Shah
  8. Eddie Stenehjem
  9. Joseph Bledsoe

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Accurate methods of identifying patients with COVID-19 who are at high risk of poor outcomes has become especially important with the advent of limited-availability therapies such as monoclonal antibodies. Here we describe development and validation of a simple but accurate scoring tool to classify risk of hospitalization and mortality.

Methods

All consecutive patients testing positive for SARS-CoV-2 from March 25-October 1, 2020 within the Intermountain Healthcare system were included. The cohort was randomly divided into 70% derivation and 30% validation cohorts. A multivariable logistic regression model was fitted for 14-day hospitalization. The optimal model was then adapted to a simple, probabilistic score and applied to the validation cohort and evaluated for prediction of hospitalization and 28-day mortality.

Results

22,816 patients were included; mean age was 40 years, 50.1% were female and 44% identified as non-white race or Hispanic/Latinx ethnicity. 6.2% required hospitalization and 0.4% died. Criteria in the simple model included: age (0.5 points per decade); high-risk comorbidities (2 points each): diabetes mellitus, severe immunocompromised status and obesity (body mass index≥30); non-white race/Hispanic or Latinx ethnicity (2 points), and 1 point each for: male sex, dyspnea, hypertension, coronary artery disease, cardiac arrythmia, congestive heart failure, chronic kidney disease, chronic pulmonary disease, chronic liver disease, cerebrovascular disease, and chronic neurologic disease. In the derivation cohort (n = 16,030) area under the receiver-operator characteristic curve (AUROC) was 0.82 (95% CI 0.81–0.84) for hospitalization and 0.91 (0.83–0.94) for 28-day mortality; in the validation cohort (n = 6,786) AUROC for hospitalization was 0.8 (CI 0.78–0.82) and for mortality 0.8 (CI 0.69–0.9).

Conclusion

A prediction score based on widely available patient attributes accurately risk stratifies patients with COVID-19 at the time of testing. Applications include patient selection for therapies targeted at preventing disease progression in non-hospitalized patients, including monoclonal antibodies. External validation in independent healthcare environments is needed.

Article activity feed

  1. Version published to 10.1371/journal.pone.0261508
  2. ScreenIT

    SciScore for 10.1101/2021.02.22.21252171: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This analysis was approved by the Institutional Review Board at Intermountain Healthcare under #1051342.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    This analysis was approved by the Institutional Review Board at Intermountain Healthcare under #1051342.
    Intermountain Healthcare
    suggested: None

    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:
    Limitations of our study include the retrospective, observational design, and the possibility that comorbidity data may have been unavailable or out of date for some patients in the cohort who receive the majority of their medical care outside our integrated healthcare system. Although the large study population and inclusion of widely recognize features improves the likelihood of generalizability, this will need to be confirmed through external validation. In this large retrospective cohort study, we identified simple risk factors that can easily be calculated at the bedside without laboratory values to risk stratify COVID-positive individuals for risk of hospitalization and death. Applications include guiding allocation of therapies that are limited in availability. External validation is needed to confirm generalizability in diverse and geographically independent population.

    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 10.1101/2021.02.22.21252171v1 on medRxiv