Pre-test probability for SARS-Cov-2-related infection score: The PARIS score

  1. Mickael Tordjman
  2. Ahmed Mekki
  3. Rahul D. Mali
  4. Ines Saab
  5. Guillaume Chassagnon
  6. Enora Guillo
  7. Robert Burns
  8. Deborah Eshagh
  9. Sebastien Beaune
  10. Guillaume Madelin
  11. Simon Bessis
  12. Antoine Feydy
  13. Fadila Mihoubi
  14. Benoit Doumenc
  15. Luc Mouthon
  16. Robert-Yves Carlier
  17. Jean-Luc Drapé
  18. Marie-Pierre Revel

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

In numerous countries, large population testing is impossible due to the limited availability of RT-PCR kits and CT-scans. This study aimed to determine a pre-test probability score for SARS-CoV-2 infection.

Methods

This multicenter retrospective study (4 University Hospitals) included patients with clinical suspicion of SARS-CoV-2 infection. Demographic characteristics, clinical symptoms, and results of blood tests (complete white blood cell count, serum electrolytes and CRP) were collected. A pre-test probability score was derived from univariate analyses of clinical and biological variables between patients and controls, followed by multivariate binary logistic analysis to determine the independent variables associated with SARS-CoV-2 infection.

Results

605 patients were included between March 10 th and April 30 th , 2020 (200 patients for the training cohort, 405 consecutive patients for the validation cohort). In the multivariate analysis, lymphocyte (<1.3 G/L), eosinophil (<0.06 G/L), basophil (<0.04 G/L) and neutrophil counts (<5 G/L) were associated with high probability of SARS-CoV-2 infection but no clinical variable was statistically significant. The score had a good performance in the validation cohort (AUC = 0.918 (CI: [0.891–0.946]; STD = 0.014) with a Positive Predictive Value of high-probability score of 93% (95%CI: [0.89–0.96]). Furthermore, a low-probability score excluded SARS-CoV-2 infection with a Negative Predictive Value of 98% (95%CI: [0.93–0.99]). The performance of the score was stable even during the last period of the study (15-30 th April) with more controls than infected patients.

Conclusions

The PARIS score has a good performance to categorize the pre-test probability of SARS-CoV-2 infection based on complete white blood cell count. It could help clinicians adapt testing and for rapid triage of patients before test results.

Article activity feed

  1. Version published to 10.1371/journal.pone.0243342
  2. ScreenIT

    SciScore for 10.1101/2020.04.28.20081687: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This retrospective observational study has been approved by our local ethic committee (Institutional Review Board N° AAA-2020–08014), which waived the need for patient consent.
    Consent: This retrospective observational study has been approved by our local ethic committee (Institutional Review Board N° AAA-2020–08014), which waived the need for patient consent.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    SPSS software (version 24, Chicago, IL, USA) was used for statistical analysis.
    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: We detected the following sentences addressing limitations in the study:
    Our study presents inherent limitations. First, its retrospective design may introduce a selection bias. Only 100 patients were included in the derivation cohort in order to recruit a similar number of infected patients and controls (50 in each group). There was no statistically significant difference between demographic characteristics of patients and controls but the 2 groups were not matched and controls more frequently presented pulmonary diseases and immunodeficiencies. A small number of patients was included due to the urgent need of diagnostic tools, but this is at risk of low reproducibility in a larger population. Indeed, the performance of the pre-test score was slightly lower in the validation cohort. We used only regular blood tests with complete white blood cell count, serum electrolytes and CRP, and did not evaluate LDH or D-dimers, since they seem to be mainly correlated with severity and have been described as risk factors for acute respiratory distress syndrome25. Another limit of this study is the only inclusion of patients from hospital wards, which probably induces a recruitment bias, since they may not be representative of patients with milder symptoms who consult their general practitioner, even though most symptomatic patients are directly referred to ED in our country. The evaluation of the performance of our pre-test probability score in a larger population would be of interest. Even though a part of patients in our cohort were admitted to Intensive C...

    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 10.1101/2020.04.28.20081687v1 on medRxiv