Evidence map›Paper›PMID 40953059›Full record

ArticlePLoS medicine2025

Effect of Paxlovid treatment during acute COVID-19 on Long COVID onset: An EHR-based target trial emulation from the N3C and RECOVER consortia.

Alexander Preiss, Abhishek Bhatia, Leyna V Aragon, John M Baratta, Monika Baskaran, Frank Blancero, Michael Daniel Brannock, Robert F Chew, Iván Díaz, Megan Fitzgerald and 8 more

Abstract read
In one paragraph

Article in PLoS medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Alexander PreissRTI International, Durham, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-7966-527X
Abhishek BhatiaUniversity of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-0063-368X
Leyna V AragonUniversity of New Mexico, Albuquerque, New Mexico, United States of America.
John M BarattaUniversity of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-1124-7928
Monika BaskaranUniversity of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Frank BlanceroRECOVER Community Representative, Bethesda, Maryland, United States of America.
Michael Daniel BrannockRTI International, Durham, North Carolina, United States of America.ORCID https://orcid.org/0000-0001-8095-547X
Robert F ChewRTI International, Durham, North Carolina, United States of America.
Iván DíazNew York University Grossman School of Medicine, New York, New York, United States of America.ORCID https://orcid.org/0000-0001-9056-2047
Megan FitzgeraldPatient Led Research Collaborative, Calabasas, California, United States of America.
Elizabeth P KellyUniversity of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-3722-8928
Andrea G ZhouUniversity of Virginia, Charlottesville, Virginia, United States of America.ORCID https://orcid.org/0000-0003-1845-5620
Thomas W CartonTulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana, United States of America.ORCID https://orcid.org/0000-0003-0059-1990
Christopher G ChuteJohns Hopkins University School of Medicine, Public Health, and Nursing, Baltimore, Maryland, United States of America.ORCID https://orcid.org/0000-0001-5437-2545
Melissa HaendelUniversity of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
Richard MoffittEmory University School of Medicine, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0003-2723-5902
Emily PfaffUniversity of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-6840-9756
N3C Consortium and the RECOVER Cohort

Funding

OTA-21-015A Post-Acute Sequelae of SARS-CoV-2 Infection Initiative: NYU Langone Health Clinical Science Core, Data Resource Core, and PASC Biorepository CoreOT2HL161847 · NHLBI · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI GROSS, RACHEL SHARON, HORWITZ, LEORA · 2021 to 2025
$651.0M
Vanderbilt Institute for Clinical and Translational Research (VICTR) -Identifying correlates of functional immunity in SARS-CoV-2 convalescent plasmaUL1TR002243 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Paul A. Harris, Wesley H Self · 2017 to 2026
$130.7M
University of Pittsburgh Clinical and Translational Science InstituteUL1TR001857 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI REIS, STEVEN E · 2016 to 2025
$129.3M
UCLA Clinical Translational Science InstituteUL1TR001881 · NCATS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI ARLEEN F. BROWN, ARASH NAEIM · 2016 to 2026
$118.1M
Clinical and Translational Science InstituteUL1TR001872 · NCATS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI COLLARD, HAROLD R, JACOBY, VANESSA · 2016 to 2025
$112.1M
Project-005UL1TR001445 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BREDELLA, MIRIAM ANTOINETTE, HOCHMAN, JUDITH S · 2015 to 2025
$103.5M
Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
Phenotypic Diversity in COVID-19UL1TR001878 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI FITZGERALD, GARRET A · 2016 to 2025
$102.4M
Transform Dissemination and Implementation Science in CTSA ProgramsUL1TR002319 · NCATS · UNIVERSITY OF WASHINGTON · PI John K. Amory · 2017 to 2026
$100.0M
Clinical and Translational Science AwardUL1TR001873 · NCATS · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI REILLY, MUREDACH P · 2016 to 2025
$99.0M
WU INSTITUTE OF CLINICAL AND TRANSLATIONAL SCIENCESUL1TR002345 · NCATS · WASHINGTON UNIVERSITY · PI William G. Powderly · 2017 to 2026
$97.8M
The Harvard Clinical and Translational Science CenterUL1TR002541 · NCATS · HARVARD MEDICAL SCHOOL · PI NADLER, LEE MARSHALL · 2018 to 2022
$93.0M
Gates Foundation INV-018455NCATS NIH HHS U24 TR002306NCATS NIH HHS UL1 TR001409NCATS NIH HHS UL1 TR001412NCATS NIH HHS UL1 TR001414NCATS NIH HHS UL1 TR001420NCATS NIH HHS UL1 TR001422NCATS NIH HHS UL1 TR001425NCATS NIH HHS UL1 TR001427NCATS NIH HHS UL1 TR001430NCATS NIH HHS UL1 TR001433NCATS NIH HHS UL1 TR001436NCATS NIH HHS UL1 TR001439NCATS NIH HHS UL1 TR001442NCATS NIH HHS UL1 TR001445NCATS NIH HHS UL1 TR001449NCATS NIH HHS UL1 TR001450NCATS NIH HHS UL1 TR001453NCATS NIH HHS UL1 TR001855NCATS NIH HHS UL1 TR001857NCATS NIH HHS UL1 TR001860NCATS NIH HHS UL1 TR001863NCATS NIH HHS UL1 TR001866NCATS NIH HHS UL1 TR001872NCATS NIH HHS UL1 TR001873NCATS NIH HHS UL1 TR001876NCATS NIH HHS UL1 TR001878NCATS NIH HHS UL1 TR001881NCATS NIH HHS UL1 TR001998NCATS NIH HHS UL1 TR002001NCATS NIH HHS UL1 TR002003NCATS NIH HHS UL1 TR002014NCATS NIH HHS UL1 TR002240NCATS NIH HHS UL1 TR002243NCATS NIH HHS UL1 TR002319NCATS NIH HHS UL1 TR002345NCATS NIH HHS UL1 TR002366NCATS NIH HHS UL1 TR002369NCATS NIH HHS UL1 TR002373NCATS NIH HHS UL1 TR002377NCATS NIH HHS UL1 TR002378NCATS NIH HHS UL1 TR002384NCATS NIH HHS UL1 TR002389NCATS NIH HHS UL1 TR002489NCATS NIH HHS UL1 TR002494NCATS NIH HHS UL1 TR002529NCATS NIH HHS UL1 TR002535NCATS NIH HHS UL1 TR002537NCATS NIH HHS UL1 TR002538NCATS NIH HHS UL1 TR002541NCATS NIH HHS UL1 TR002544NCATS NIH HHS UL1 TR002548NCATS NIH HHS UL1 TR002550NCATS NIH HHS UL1 TR002553NCATS NIH HHS UL1 TR002556NCATS NIH HHS UL1 TR002645NCATS NIH HHS UL1 TR002649NCATS NIH HHS UL1 TR002733NCATS NIH HHS UL1 TR002736NCATS NIH HHS UL1 TR003015NCATS NIH HHS UL1 TR003017NCATS NIH HHS UL1 TR003096NCATS NIH HHS UL1 TR003098NCATS NIH HHS UL1 TR003107NCATS NIH HHS UL1 TR003142NCATS NIH HHS UL1 TR003167NCATS NIH HHS UM1 TR004406NCATS NIH HHS UM1 TR004528NCATS NIH HHS UM1 TR004556NHLBI NIH HHS OT2 HL161847NIGMS NIH HHS U54 GM104938NIGMS NIH HHS U54 GM104940NIGMS NIH HHS U54 GM104941NIGMS NIH HHS U54 GM104942NIGMS NIH HHS U54 GM115371NIGMS NIH HHS U54 GM115428NIGMS NIH HHS U54 GM115458NIGMS NIH HHS U54 GM115516NIGMS NIH HHS U54 GM115677NIGMS NIH HHS U54 GM133807
6 · The paper itself

Abstract

backgroundPreventing and treating post-acute sequelae of COVID-19 infection (PASC), commonly known as Long COVID, has become a public health priority. This study tests whether Paxlovid treatment in the acute phase of COVID-19 could help prevent the onset of PASC. METHODS AND

findingsWe used electronic health records from the National Clinical Cohort Collaborative to define a cohort of 445,738 patients who had COVID-19 since April 1, 2022, and were eligible for Paxlovid treatment due to risk for progression to severe COVID-19. We used the target trial emulation framework to estimate the effect of Paxlovid treatment on PASC incidence. We emulated a series of six sequential trials: one for each day of a 5-day treatment grace period. For each sequential trial, the treatment group was defined as patients prescribed Paxlovid on the trial start day, and the control group was defined as all patients meeting eligibility criteria who remained untreated on the trial start day. We pooled individual record-level data from the sequential trials for analysis. The follow-up period was 180 days. The primary outcome was overall PASC incidence measured using a computable phenotype. Secondary outcomes were incident cognitive, fatigue, and respiratory symptoms in the post-acute period. We controlled for a wide range of demographic and medical history covariates. Compared to the control group, Paxlovid treatment did not have a significant effect on overall PASC incidence or incident respiratory symptoms. It had a small protective effect against cognitive (relative risk [RR] 0.91; 95% CI [0.84, 0.98]; p = 0.019) and fatigue (RR 0.94; 95% CI [0.90, 0.98]; p = 0.002) symptoms. Finally, we estimated Paxlovid's effect on overall PASC incidence across strata of age, COVID-19 vaccination status, and Charlson Comorbidity Index (CCI) prior to COVID-19. We found small protective effects among patients aged 65 years or more (RR 0.92; 95% CI [0.88, 0.97]; p < 0.001; absolute risk difference [ARD] -0.43%; number needed to treat [NNT] 233) and with a CCI of 3 or 4 (RR 0.83; 95% CI [0.75, 0.92]; p < 0.001; ARD -1.30%; NNT 76). This study's main limitation is that the causal interpretation relies on the assumption that we controlled for all confounding variables.

conclusionsAlthough some prior observational studies suggested that Paxlovid held promise as a PASC preventive, this study-with a large, nationally sampled cohort; a contemporary study period; and causal inference methodology-found that Paxlovid treatment during acute COVID-19 had no effect on subsequent PASC incidence. Stratified analyses suggest that Paxlovid may have a small protective effect among higher-risk patients, but the NNT is high. In conclusion, we see Paxlovid as unlikely to become a definitive solution for PASC prevention.

Indexed as

COVID-19COVID-19 Drug TreatmentAdultAgedElectronic Health RecordsFemaleHumansIncidenceMaleMiddle AgedPost-Acute COVID-19 SyndromeSARS-CoV-2

Identifiers

PMID40953059
PMCPMC12445499

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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.