Modeling SARS-CoV-2 viral kinetics and association with mortality in hospitalized patients from the French COVID cohort

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Abstract
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 <65 y and ≥65 y, respectively. Assuming that the association between viral dynamics and mortality would remain similar to that observed in our population, this could translate into a reduction of mortality from 19 to 14% in patients of age ≥65 y with risk factors. Our results show that viral dynamics is associated with mortality in hospitalized patients. Strategies aiming to reduce viral load could have an effect on mortality rate in this population. 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.
Funding Information
  • Agence Nationale de la Recherche (ANR-20-COVI-0018)
  • Bill and Melinda Gates Foundation (INV-017335)
  • French Ministry of Health (PHRC n{degree sign}20-0424)

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