COVID-MATCH65—A prospectively derived clinical decision rule for severe acute respiratory syndrome coronavirus 2

  1. Jason A. Trubiano
  2. Sara Vogrin
  3. Olivia C. Smibert
  4. Nada Marhoon
  5. Adrian A. Alexander
  6. Kyra Y. L. Chua
  7. Fiona L. James
  8. Nicholas R. L. Jones
  9. Sam E. Grigg
  10. Cecilia L. H. Xu
  11. Nasreen Moini
  12. Sam R. Stanley
  13. Michael T. Birrell
  14. Morgan T. Rose
  15. Claire L. Gordon
  16. Jason C. Kwong
  17. Natasha E. Holmes

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Abstract

We report on the key clinical predictors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and present a clinical decision rule that can risk stratify patients for COVID-19.

Design, participants and setting

A prospective cohort of patients assessed for COVID-19 at a screening clinic in Melbourne, Australia. The primary outcome was a positive COVID-19 test from nasopharyngeal swab. A backwards stepwise logistic regression was used to derive a model of clinical variables predictive of a positive COVID-19 test. Internal validation of the final model was performed using bootstrapped samples and the model scoring derived from the coefficients, with modelling performed for increasing prevalence.

Results

Of 4226 patients with suspected COVID-19 who were assessed, 2976 patients underwent SARS-CoV-2 testing (n = 108 SARS-CoV-2 positive) and were used to determine factors associated with a positive COVID-19 test. The 7 features associated with a positive COVID-19 test on multivariable analysis were: C OVID-19 patient exposure or international travel, M yalgia/malaise, A nosmia or ageusia, T emperature, C oryza/sore throat, H ypoxia–oxygen saturation < 97%, 65 years or older—summarized in the mnemonic C OVID- MATCH65 . Internal validation showed an AUC of 0.836. A cut-off of ≥ 1.5 points was associated with a 92.6% sensitivity and 99.5% negative predictive value (NPV) for COVID-19.

Conclusions

From the largest prospective outpatient cohort of suspected COVID-19 we define the clinical factors predictive of a positive SARS-CoV-2 test. The subsequent clinical decision rule, COVID-MATCH65, has a high sensitivity and NPV for SARS-CoV-2 and can be employed in the pandemic, adjusted for disease prevalence, to aid COVID-19 risk-assessment and vital testing resource allocation.

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  1. ScreenIT

    SciScore for 10.1101/2020.06.30.20143818: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the Austin Health Human Research and Ethics Committee.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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:
    Our model has some limitations, including the single centre prospective data source, jurisdictional guided testing criteria, testing of symptomatic only patients and absence of external validation. However, only one small retrospective US cohort (n =49 COVID-19 positive /n= 98 COVID-19 negative)(9) and two non-peer reviewed publications from China have examined the role of clinical decision rules from large datasets - Meng et al. (n = 620 outpatients; 48.7% positive)(10) and Song et al.(11) (n = 304 inpatients; 24.0% positive), both limited by requirement for clinical and laboratory parameters. COVID-MATCH65 uses readily available clinical information without laboratory test results, with a score of 1.5 associated with high sensitivity and NPV, enabling application in the outpatient and potentially early inpatient setting. Further risk stratification can be made with the scoring tool (lowest risk [< 1 in 100] to extreme risk [1 in 1]), aiding diagnostic approaches in patients with suspected COVID-19. In a pandemic where diagnostic resources are limited in both low- and high-income settings,(12) risk stratification of those likely to have COVID-19 is urgently required and tools such as COVID-MATCH65 can aid the front-line clinician. We encourage readers to urgently employ and validate COVID-MATCH65 in their own datasets, as it is likely to aid clinicians at point-of-care especially via an open access web platform (http://COVID-MATCH65.austin.org.au).

    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.
    • Thank you for including a protocol registration statement.

    About SciScore

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  2. Version published to 10.1371/journal.pone.0243414
  3. Version published to 10.1101/2020.06.30.20143818v1 on medRxiv