Development and Validation of a Simple Risk Score for Diagnosing COVID-19 in the Emergency Room

  1. Joowhan Sung
  2. Naveed Choudry
  3. Rima Bachour

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Abstract

As the COVID-19 pandemic continues to escalate and place pressure on hospital system resources, a proper screening and risk stratification score is essential. We aimed to develop a risk score to identify patients with increased risk of COVID-19, allowing proper identification and allocation of limited resources. A retrospective study was conducted of 338 patients who were admitted to the hospital from the emergency room and tested for COVID-19 at an acute care hospital in the Metropolitan Washington D.C. area. The dataset was split into development and validation sets with a ratio of 6:4. Demographics, presenting symptoms, sick contact, triage vital signs, initial laboratory and chest X-ray results were analyzed to develop a prediction model for COVID-19 diagnosis. Multivariable logistic regression was performed in a stepwise fashion to develop a prediction model, and a scoring system was created based on the coefficients of the final model. Among 338 patients admitted to the hospital from the emergency room, 136 (40.2%) patients tested positive for COVID-19 and 202 (59.8%) patients tested negative. Nursing facility residence (2 points), sick contact (2 points), constitutional symptom (1 point), respiratory symptom (1 point), gastrointestinal symptom (1 point), obesity (1 point), hypoxia at triage (1 point), and leukocytosis (−1 point) were included in the prediction score. A risk score for COVID-19 diagnosis achieved AUROC of 0.87 (95% CI 0.83-0.92) in the development dataset and 0.83 (95% CI 0.76-0.90) in the validation dataset. A risk prediction score for COVID-19 can be used as a supplemental tool to assist clinical decision to triage, test, and quarantine patients admitted to the hospital from the emergency room.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.11.20173112: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical consideration: This study was approved by the Institutional Review Board (IRB) of the MedStar Health Research Institute with a waiver of individual consents.
    RandomizationDevelopment of prediction model: The dataset was randomly split into a development cohort and a validation cohort with a 6:4 ratio using function of statistical software.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The analysis was performed using STATA version 15.1 (STATA Corp., Texas, USA).
    STATA
    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: We detected the following sentences addressing limitations in the study:
    Nonetheless, our study has limitations. Our study is limited by a small cohort size. In this study, we did not find a significant association between chest X ray findings and COVID-19 status after adjusting for the effects from confounders. Chest X ray results are likely to have clinical utility in risk-stratification of COVID-19 patients, but our study was not sufficiently powered to detect this difference. Inflammatory markers such as d-dimer, C-reactive protein, and ferritin were reported to be often elevated in COVID-19 but these lab values were not available for many study patients and therefore not included in the model [13, 14]. As our scoring system was developed in a cohort of patients admitted to the regular medical floor from the emergency room, our study result cannot be generalized to other setting, such as outpatient practices, urgent cares or intensive care units. SARS-CoV-2 is also known to cause asymptomatic infection, and our score system is designed to risk stratify newly admitted patients with symptoms concerning for COVID-19 infection, therefore cannot be used to identify asymptomatic patients. Given the above limitations and a single center study design, the risk score should be further validated in larger and/or multicenter studies.

    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 10.1101/2020.08.11.20173112v1 on medRxiv
  3. Version published to 10.1017/s0950268820002769