Predictive usefulness of RT-PCR testing in different patterns of Covid-19 symptomatology: analysis of a French cohort of 12,810 outpatients

  1. The AP-HP/Universities/Inserm COVID-19 Research Collaboration
  2. Writing Committee
  3. Caroline Apra
  4. Charlotte Caucheteux
  5. Arthur Mensch
  6. Jenny Mansour
  7. Mélodie Bernaux
  8. Agnès Dechartres
  9. Erwan Debuc
  10. Xavier Lescure
  11. Aurélien Dinh
  12. Youri Yordanov
  13. Patrick Jourdain
  14. Data Science Committee
  15. Arthur Mensch
  16. Charlotte Caucheteux
  17. Caroline Apra
  18. Jenny Mansour
  19. Nicolas Paris
  20. Alexandre Gramfort
  21. Scientific Committee
  22. Amélie Aime-Eusebi
  23. Caroline Apra
  24. Alexandre Bleibtreu
  25. Erwan Debuc
  26. Agnès Dechartres
  27. Laurène Deconinck
  28. Aurélien Dinh
  29. Patrick Jourdain
  30. Christine Katlama
  31. Josselin Lebel
  32. François-Xavier Lescure
  33. Youri Yordanov
  34. Covidom Regional Center Steering Committee
  35. Yves Artigou
  36. Amélie Banzet
  37. Elodie Boucheron
  38. Christiane Boudier
  39. Edouard Buzenac
  40. Marie-Claire Chapron
  41. Dalhia Chekaoui
  42. Laurent De Bastard
  43. Erwan Debuc
  44. Aurélien Dinh
  45. Alexandre Grenier
  46. Pierre-Etienne Haas
  47. Julien Hody
  48. Michèle Jarraya
  49. Patrick Jourdain
  50. Louis Lacaille
  51. Aurélie Le Guern
  52. Jeremy Leclert
  53. Fanny Male
  54. Jerôme Marchand-Arvier
  55. Emmanuel Martin-Blondet
  56. Apolinne Nassour
  57. Oussama Ourahou
  58. Thomas Penn
  59. Ambre Ribardiere
  60. Nicolas Robin
  61. Camille Rouge
  62. Nicolas Schmidt
  63. Pascaline Villie

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Abstract

Reverse transcriptase polymerase chain reaction (RT-PCR) is a key tool to diagnose Covid-19. Yet it may not be the most efficient test in all patients. In this paper, we develop a clinical strategy for prescribing RT-PCR to patients based on data from COVIDOM, a French cohort of 54,000 patients with clinically suspected Covid-19, including 12,810 patients tested by RT-PCR. We use a machine-learning algorithm (decision tree) in order to predict RT-PCR results based on the clinical presentation. We show that symptoms alone are sufficient to predict RT-PCR outcome with a mean average precision of 86%. We identify combinations of symptoms that are predictive of RT-PCR positivity (90% for anosmia/ageusia) or negativity (only 30% of RT-PCR+ for a subgroup with cardiopulmonary symptoms): in both cases, RT-PCR provides little added diagnostic value. We propose a prescribing strategy based on clinical presentation that can improve the global efficiency of RT-PCR testing.

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  1. Version published to 10.1038/s41598-021-99991-6
  2. ScreenIT

    SciScore for 10.1101/2020.06.07.20124438: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: During registration, patients provided an electronic consent for the Covidom telemonitoring program and they were informed of the potential use of anonymized data for research purposes.
    IACUC: This use was approved by the Scientific and ethical committee of APHP (IRB00011591).
    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: 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 10.1101/2020.06.07.20124438v1 on medRxiv