Evidence map›Paper›PMID 38622989›Full record

ArticleeLife2024

A retrospective cohort study of Paxlovid efficacy depending on treatment time in hospitalized COVID-19 patients.

Zhanwei Du, Lin Wang, Yuan Bai, Yunhu Liu, Eric H Y Lau, Alison P Galvani, Robert M Krug, Benjamin John Cowling, Lauren A Meyers

Abstract read
In one paragraph

Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zhanwei Du *WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China.ORCID https://orcid.org/0000-0002-2020-767X
Lin Wang *Department of Genetics, University of Cambridge, Cambridge, United Kingdom.ORCID https://orcid.org/0000-0002-5371-2138
Yuan Bai *WHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China.
Yunhu LiuWHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China.
Eric H Y LauWHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China.
Alison P GalvaniCenter for Infectious Disease Modeling and Analysis, Yale School of Public Health, New Haven, United States.
Robert M KrugDepartment of Molecular Biosciences, John Ring LaMontagne Center for Infectious Disease Institute for Cellular and Molecular Biology, University of Texas at Austin, Austin, United States.ORCID https://orcid.org/0000-0002-3754-5034
Benjamin John CowlingWHO Collaborating Center for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong Special Administrative Region, Hong Kong, China.ORCID https://orcid.org/0000-0002-6297-7154
Lauren A MeyersDepartment of Integrative Biology, University of Texas at Austin, Austin, United States.ORCID https://orcid.org/0000-0002-5828-8874

Funding

Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial dataR01AI151176 · NIAID · YALE UNIVERSITY · PI GALVANI, ALISON P, MEYERS, LAUREN ANCEL · 2020 to 2024
$3.9M
ACL HHS U01IP001136CDC HHS U01IP001136NIAID NIH HHS R01 AI151176NIH HHS AI151176
6 · The paper itself

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.

Indexed as

Antiviral AgentsCOVID-19COVID-19 Drug TreatmentSARS-CoV-2AdultAgedDrug CombinationsFemaleHong KongHospitalizationHumansMaleMiddle AgedRetrospective StudiesTime FactorsTreatment OutcomeAntiviral AgentsDrug CombinationsantiviralCOVID-19epidemiologyglobal healthPaxlovidSARS-CoV-2transmissionviruses

Identifiers

PMID38622989
PMCPMC11078542

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.