A retrospective cohort study of Paxlovid efficacy depending on treatment time in hospitalized COVID-19 patients
Abstract
Paxlovid, a SARS-CoV-2 antiviral, not only prevents severe illness but also curtails viral shedding, lowering transmission risks from treated patients. By fitting a mathematical model of within-host Omicron viral dynamics to electronic health records data from 208 hospitalized patients in Hong Kong, we estimate that Paxlovid can inhibit over 90% of viral replication. However, its effectiveness critically depends on the timing of treatment. If treatment is initiated three days after symptoms first appear, we estimate a 17% chance of a post-treatment viral rebound and a 12% (95% CI: 0%-16%) reduction in overall infectiousness for non-rebound cases. Earlier treatment significantly elevates the risk of rebound without further reducing infectiousness, whereas starting beyond five days reduces its efficacy in curbing peak viral shedding. Among the 104 patients who received Paxlovid, 62% began treatment within an optimal three-to-five-day day window after symptoms appeared. Our findings indicate that broader global access to Paxlovid, coupled with appropriately timed treatment, can mitigate the severity and transmission of SARS-Cov-2.
Data availability
All data used in this study can be accessed through Github: https://github.com/ZhanweiDU/PaxHK/.
Article and author information
Author details
Funding
Innovation and Technology Commission (AIR@InnoHK)
- Zhanwei Du
Health and Medical Research Fund (07210147)
- Zhanwei Du
National Natural Science Foundation of China (82304204)
- Yuan Bai
National Institutes of Health (AI151176)
- Lauren A Meyers
Centers for Disease Control and Prevention (U01IP001136)
- Lauren A Meyers
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- James M McCaw, University of Melbourne, Australia
Ethics
Human subjects: Individual patient-informed consent was not required in this study using anonymized data.
Version history
- Received: May 31, 2023
- Accepted: April 3, 2024
- Accepted Manuscript published: April 16, 2024 (version 1)
- Version of Record published: May 8, 2024 (version 2)
Copyright
© 2024, Du et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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