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Modeling SARS-CoV-2 viral kinetics and association with mortality in hospitalized patients from the French COVID cohort
- Source :
- Proceedings of the National Academy of Sciences of the United States of America, Proceedings of the National Academy of Sciences of the United States of America, 2021, 118 (8), pp.e2017962118. ⟨10.1073/pnas.2017962118⟩, Proceedings of the National Academy of Sciences of the United States of America, National Academy of Sciences, 2021, 118 (8), pp.e2017962118. ⟨10.1073/pnas.2017962118⟩, Proceedings of the National Academy of Sciences
- Publication Year :
- 2021
- Publisher :
- Proceedings of the National Academy of Sciences, 2021.
-
Abstract
- Significance A detailed characterization of viral load kinetics and its association with disease evolution is key to understand the virus pathogenesis, identify high-risk patients, and design better treatment strategies. We here analyze the mortality and the virological information collected in 655 hospitalized patients, including 284 with longitudinal measurements, and we build a mathematical model of virus dynamics and survival. We predict that peak viral load occurs 1 d before symptom onset, on average, and that dynamics of decline after peak is slower in older patients. Viral load dynamics after hospital admission is an independent predictor of the risk of death, suggesting that prolonged viral shedding of high quantities of virus is associated with poor outcome in this population.<br />The characterization of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral kinetics in hospitalized patients and its association with mortality is unknown. We analyzed death and nasopharyngeal viral kinetics in 655 hospitalized patients from the prospective French COVID cohort. The model predicted a median peak viral load that coincided with symptom onset. Patients with age ≥65 y had a smaller loss rate of infected cells, leading to a delayed median time to viral clearance occurring 16 d after symptom onset as compared to 13 d in younger patients (P < 10−4). In multivariate analysis, the risk factors associated with mortality were age ≥65 y, male gender, and presence of chronic pulmonary disease (hazard ratio [HR] > 2.0). Using a joint model, viral dynamics after hospital admission was an independent predictor of mortality (HR = 1.31, P < 10−3). Finally, we used our model to simulate the effects of effective pharmacological interventions on time to viral clearance and mortality. A treatment able to reduce viral production by 90% upon hospital admission would shorten the time to viral clearance by 2.0 and 2.9 d in patients of age
- Subjects :
- Male
0301 basic medicine
Medical Sciences
viruses
MESH: Hospitalization
Antibodies, Viral
0302 clinical medicine
Risk Factors
MESH: Risk Factors
Nasopharynx
Epidemiology
MESH: COVID-19
Prospective Studies
030212 general & internal medicine
MESH: Models, Theoretical
Prospective cohort study
MESH: Aged
education.field_of_study
[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology
Multidisciplinary
MESH: Kinetics
Mortality rate
Biological Sciences
Viral Load
Prognosis
viral dynamics
3. Good health
Hospitalization
Survival Rate
[SDV.MP]Life Sciences [q-bio]/Microbiology and Parasitology
MESH: RNA, Viral
Cohort
RNA, Viral
Female
France
MESH: Viral Load
Viral load
medicine.medical_specialty
MESH: Survival Rate
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
Population
MESH: Prognosis
03 medical and health sciences
Internal medicine
medicine
Humans
MESH: SARS-CoV-2
education
[SDV.MP] Life Sciences [q-bio]/Microbiology and Parasitology
Survival rate
Aged
MESH: Humans
SARS-CoV-2
business.industry
COVID-19
Models, Theoretical
mortality
MESH: Male
MESH: Nasopharynx
MESH: Prospective Studies
MESH: France
Kinetics
030104 developmental biology
business
MESH: Female
MESH: Antibodies, Viral
[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Subjects
Details
- ISSN :
- 10916490 and 00278424
- Volume :
- 118
- Database :
- OpenAIRE
- Journal :
- Proceedings of the National Academy of Sciences
- Accession number :
- edsair.doi.dedup.....38f951d13c594c4dfe351e42c46e8663