Association between SARS‐CoV‐2 viral kinetics and clinical score evolution in hospitalized patients

Author:

Néant Nadège1,Lingas Guillaume1,Gaymard Alexandre23ORCID,Belhadi Drifa14,Hites Maya5,Staub Thérèse6,Greil Richard789,Paiva Jose‐Artur1011,Poissy Julien12,Peiffer‐Smadja Nathan11314,Costagliola Dominique15,Yazdanpanah Yazdan113,Bouscambert‐Duchamp Maude2,Gagneux‐Brunon Amandine161718,Ader Florence1920,Mentré France14ORCID,Wallet Florent21ORCID,Burdet Charles14,Guedj Jérémie1ORCID,

Affiliation:

1. IAME Université Paris Cité, IAME, Inserm, F‐75018 Paris France

2. Laboratoire de Virologie, Institut des Agents Infectieux de Lyon Centre National de Référence des Virus Respiratoires France Sud, Hospices Civils de Lyon Lyon France

3. Laboratoire Virpath Université de Lyon, Virpath, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon, Université Claude Bernard Lyon 1 Lyon France

4. Département d'Épidémiologie AP‐HP, Hôpital Bichat, Biostatistique et Recherche Clinique Paris France

5. Hôpital de Bruxelles‐Érasme Université Libre de Bruxelles, Clinique des Maladies Infectieuses Brussels Belgium

6. Centre Hospitalier de Luxembourg, Service des Maladies Infectieuses Luxembourg Luxembourg

7. Department of Internal Medicine III with Haematology, Medical Oncology, Aemostaseology, Infectiology and Rheumatology, Oncologic Center Salzburg Cancer Research Institute–Laboratory for Immunological and Molecular Cancer Research, Paracelsus Medical University Salzburg Salzburg Austria

8. Cancer Cluster Salzburg Salzburg Austria

9. AGMT Salzburg Austria

10. Emergency and Intensive Care Department, Centro Hospitalar São João Porto Portugal

11. Faculty of Medicine Universidade do Porto Porto Portugal

12. Intensive Care Department Université de Lille, Inserm U1285, CHU Lille, Pôle de Réanimation, CNRS, UMR 8576–UGSF–Unité de Glycobiologie Structurale et Fonctionnelle Lille France

13. AP‐HP, Hôpital Bichat, Service de Maladies Infectieuses et Tropicales Paris France

14. National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Imperial College London London UK

15. Sorbonne Université, Inserm, Institut Pierre‐Louis d'Épidémiologie et de Santé Publique Paris France

16. CHU de Saint‐Etienne, Service d'Infectiologie Saint‐Etienne France

17. Université Jean Monnet, Université Claude Bernard Lyon 1, GIMAP, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon Saint‐Etienne France

18. CIC 1408, INSERM Saint‐Etienne France

19. Département des Maladies Infectieuses et Tropicales Hospices Civils de Lyon Lyon France

20. Département des Maladies Infectieuses et Tropicales Université Claude Bernard Lyon 1, CIRI, INSERM U1111, CNRS UMR5308, ENS Lyon Lyon France

21. Service de Médecine Intensive Réanimation Anesthésie, Centre Hospitalier Lyon Sud Hospices Civils de Lyon Pierre‐Benite France

Abstract

AbstractThe role of antiviral treatment in coronavirus disease 2019 hospitalized patients is controversial. To address this question, we analyzed simultaneously nasopharyngeal viral load and the National Early Warning Score 2 (NEWS‐2) using an effect compartment model to relate viral dynamics and the evolution of clinical severity. The model is applied to 664 hospitalized patients included in the DisCoVeRy trial (NCT04315948; EudraCT 2020‐000936‐23) randomly assigned to either standard of care (SoC) or SoC + remdesivir. Then we use the model to simulate the impact of antiviral treatments on the time to clinical improvement, defined by a NEWS‐2 score lower than 3 (in patients with NEWS‐2 <7 at hospitalization) or 5 (in patients with NEWS‐2 ≥7 at hospitalization), distinguishing between patients with low or high viral load at hospitalization. The model can fit well the different observed patients trajectories, showing that clinical evolution is associated with viral dynamics, albeit with large interindividual variability. Remdesivir antiviral activity was 22% and 78% in patients with low or high viral loads, respectively, which is not sufficient to generate a meaningful effect on NEWS‐2. However, simulations predicted that antiviral activity greater than 99% could reduce by 2 days the time to clinical improvement in patients with high viral load, irrespective of the NEWS‐2 score at hospitalization, whereas no meaningful effect was predicted in patients with low viral loads. Our results demonstrate that time to clinical improvement is associated with time to viral clearance and that highly effective antiviral drugs could hasten clinical improvement in hospitalized patients with high viral loads.

Publisher

Wiley

Subject

Pharmacology (medical),Modeling and Simulation

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