Simple risk scores to predict hospitalization or death in outpatients with COVID-19 including the Omicron variant

  1. Mark H. Ebell
  2. Roya Hamadani
  3. Autumn Kieber-Emmons

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Abstract

Importance

Outpatient physicians need guidance to support their clinical decisions regarding management of patients with COVID-19, specifically whether to hospitalize a patient or if managed as an outpatient, how closely to follow them.

Objective

To develop and prospectively validate a clinical prediction rule to predict the likelihood of hospitalization for outpatients with COVID-19 that does not require laboratory testing or imaging, including during the current Omicron wave.

Design

Derivation and temporal validation of a clinical prediction rule, and prospective validation of two externally derived clinical prediction rules.

Setting

Primary and urgent care clinics in a Pennsylvania health system.

Participants

Patients 12 years and older presenting to outpatient clinics who had a positive polymerase chain reaction test for COVID-19.

Main outcomes and measures

Classification accuracy (percentage in each risk group hospitalized) and area under the receiver operating characteristic curve (AUC).

Results

Overall, 4.0% of outpatients in the early derivation cohort (5843 patients presenting before 3/1/21), 4.2% in the late validation cohort (3806 patients presenting 3/1/21 to 9/30/21), and 1.9% in an Omicron cohort were ultimately hospitalized. We developed and temporally validated four simple risk scores. The base score included age, dyspnea, and the presence of a comorbidity, with the other scores adding fever, respiratory rate and/or oxygen saturation. All had very good overall accuracy (AUC 0.85-0.87) and classified at least half of patients into a low risk with a < 1% likelihood of hospitalization. Hospitalization rates in the Omicron cohort were 0.22%, 1.3% and 8.7% for the base score. Two externally derived risk scores identified more low risk patients, but with a higher overall risk of hospitalization than our novel risk scores.

Conclusions and relevance

A simple risk score applicable to outpatient and telehealth settings can classify over half of COVID-19 outpatients into a very low risk group with a 0.22% hospitalization risk in the Omicron cohort. The Lehigh Outpatient COVID Hospitalization (LOCH) risk score is available online as a free app: https://ebell-projects.shinyapps.io/LehighRiskScore/ .

Key points

Question

Is it possible to predict the eventual likelihood of hospitalization for outpatients with COVID-19 using simple non-laboratory based risk scores?

Findings

We created and temporally validated in the same population 4 risk scores with 3 to 5 predictors that do not require laboratory testing. Groups with low (0.34% to 0.89%), moderate (4.0% to 6.2%), and high-risk (19.2% to 25.2%) of hospitalization were identified. The risk scores were also accurate in an Omicron dominant cohort with hospitalization rates of 0.22% to 0.43% in the low-risk groups, 1.3% to 1.7% in the moderate risk groups, and 8.7% to 15.3% in the high risk groups.

Meaning

Simple risk scores can help support decisions about hospitalization in the outpatient setting.

Article activity feed

  1. Version published to 10.1101/2022.01.14.22269295v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2022.01.14.22269295: (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
    17 (StataCorp, College Station, Texas).
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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.

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  3. Version published to 10.1101/2022.01.14.22269295v1 on medRxiv