Rapid identification of SARS-CoV-2-infected patients at the emergency department using routine testing

  1. Steef Kurstjens
  2. Armando van der Horst
  3. Robert Herpers
  4. Mick W. L. Geerits
  5. Yvette C. M. Kluiters-de Hingh
  6. Eva-Leonne Göttgens
  7. Martinus J. T. Blaauw
  8. Marc H. M. Thelen
  9. Marc G. L. M. Elisen
  10. Ron Kusters

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Abstract

Objectives

The novel coronavirus disease 19 (COVID-19), caused by SARS-CoV-2, spreads rapidly across the world. The exponential increase in the number of cases has resulted in overcrowding of emergency departments (ED). Detection of SARS-CoV-2 is based on an RT-PCR of nasopharyngeal swab material. However, RT-PCR testing is time-consuming and many hospitals deal with a shortage of testing materials. Therefore, we aimed to develop an algorithm to rapidly evaluate an individual’s risk of SARS-CoV-2 infection at the ED.

Methods

In this multicenter retrospective study, routine laboratory parameters (C-reactive protein, lactate dehydrogenase, ferritin, absolute neutrophil and lymphocyte counts), demographic data and the chest X-ray/CT result from 967 patients entering the ED with respiratory symptoms were collected. Using these parameters, an easy-to-use point-based algorithm, called the corona-score, was developed to discriminate between patients that tested positive for SARS-CoV-2 by RT-PCR and those testing negative. Computational sampling was used to optimize the corona-score. Validation of the model was performed using data from 592 patients.

Results

The corona-score model yielded an area under the receiver operating characteristic curve of 0.91 in the validation population. Patients testing negative for SARS-CoV-2 showed a median corona-score of 3 vs. 11 (scale 0–14) in patients testing positive for SARS-CoV-2 (p<0.001). Using cut-off values of 4 and 11 the model has a sensitivity and specificity of 96 and 95%, respectively.

Conclusions

The corona-score effectively predicts SARS-CoV-2 RT-PCR outcome based on routine parameters. This algorithm provides the means for medical professionals to rapidly evaluate SARS-CoV-2 infection status of patients presenting at the ED with respiratory symptoms.

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  1. Version published to 10.1515/cclm-2020-0593
  2. ScreenIT

    SciScore for 10.1101/2020.04.20.20067512: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Clinical chemistry parameters (C-reactive protein (CRP), ferritin, lactate dehydrogenase (LDH), absolute lymphocyte and neutrophil counts (ALC and ANC)) were obtained on routine analyzers from Siemens (Jeroen Bosch Hospital and the (immuno-)chemistry of Bernhoven Hospital), Sysmex (Elisabeth TweeSteden Hospital and the hematology of Amphia Hospital), Roche (Elisabeth TweeSteden Hospital and the (immuno-)chemistry of Amphia Hospital) and Abbott (hematology of Bernhoven Hospital).
    Abbott
    suggested: (Abbott, RRID:SCR_010477)
    v3.7.0, Python Software Foundation, USA) to optimize for a maximum area under the receiver operating characteristic (AUROC) curve.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Statistical analyses: Data were analyzed using the Excel 2010 (Microsoft Corporation, USA) plugin ‘Analyse-it v5.11’ (
    Excel
    suggested: None
    (Analyse-it Software, Ltd, UK) and SPSS statistics v22 (IBM, USA).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    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.

    About SciScore

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  3. Version published to 10.1101/2020.04.20.20067512v1 on medRxiv