Evidence map›Paper›PMID 39823513›Full record

ArticlePLoS medicine2025

Effect of nirmatrelvir/ritonavir (Paxlovid) on hospitalization among adults with COVID-19: An electronic health record-based target trial emulation from N3C.

Abhishek Bhatia, Alexander J Preiss, Xuya Xiao, M Daniel Brannock, G Caleb Alexander, Robert F Chew, Hannah Davis, Megan Fitzgerald, Elaine Hill, Elizabeth P Kelly 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 18 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 2 pooled it
–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

18 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

18 authors.

Abhishek BhatiaUniversity of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-0063-368X
Alexander J PreissRTI International, Durham, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-7966-527X
Xuya XiaoSchool of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID https://orcid.org/0000-0003-4198-7502
M Daniel BrannockRTI International, Durham, North Carolina, United States of America.ORCID https://orcid.org/0000-0001-8095-547X
G Caleb AlexanderSchool of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, United States of America.
Robert F ChewRTI International, Durham, North Carolina, United States of America.ORCID https://orcid.org/0000-0002-6979-1766
Hannah DavisPatient-Led Research Collaborative, New York, New York State, United States of America.ORCID https://orcid.org/0000-0002-1245-2034
Megan FitzgeraldPatient-Led Research Collaborative, New York, New York State, United States of America.
Elaine HillUniversity of Rochester, Department of Public Health Sciences and Department of Economics, Rochester, New York State, United States of America.ORCID https://orcid.org/0000-0003-2494-317X
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
Hemalkumar B MehtaSchool of Medicine, Public Health, and Nursing, Johns Hopkins University, Baltimore, Maryland, United States of America.ORCID https://orcid.org/0000-0001-9134-6370
Charisse Madlock-BrownUniversity of Tennessee Health Science Center, Memphis, Tennessee, United States of America.ORCID https://orcid.org/0000-0002-3647-1045
Kenneth J WilkinsNational Institute of Diabetes & Digestive & Kidney Diseases, Office of the Director, National Institutes of Health, Bethesda, Maryland, United States of America.ORCID https://orcid.org/0000-0003-0531-7165
Christopher G ChuteSchool of Medicine, Public Health, and Nursing, Johns Hopkins University, 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.ORCID https://orcid.org/0000-0001-9114-8737
Richard MoffittEmory University, Atlanta, Georgia, United States of America.ORCID https://orcid.org/0000-0003-2723-5902
Emily R 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

Funding

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
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
Implementing a Maternal health and PRegnancy Outcomes Vision for Everyone (IMPROVE)UL1TR002378 · NCATS · EMORY UNIVERSITY · PI Andres J Garcia, Elizabeth O. Ofili · 2017 to 2026
$92.1M
UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
Bill & Melinda 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 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 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 TR004556NIA NIH HHS K01 AG070329NIDA NIH HHS U01 DA055358NIDDK NIH HHS K23 DK124654NIGMS 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 GM115677
6 · The paper itself

Abstract

backgroundNirmatrelvir with ritonavir (Paxlovid) is indicated for patients with Coronavirus Disease 2019 (COVID-19) who are at risk for progression to severe disease due to the presence of one or more risk factors. Millions of treatment courses have been prescribed in the United States alone. Paxlovid was highly effective at preventing hospitalization and death in clinical trials. Several studies have found a protective association in real-world data, but they variously used less recent study periods, correlational methods, and small, local cohorts. Their estimates also varied widely. The real-world effectiveness of Paxlovid remains uncertain, and it is unknown whether its effect is homogeneous across demographic strata. This study leverages electronic health record data in the National COVID Cohort Collaborative's (N3C) repository to investigate disparities in Paxlovid treatment and to emulate a target trial assessing its effectiveness in reducing severe COVID-19 outcomes. METHODS AND

findingsThis target trial emulation used a cohort of 703,647 patients with COVID-19 seen at 34 clinical sites across the United States between April 1, 2022 and August 28, 2023. Treatment was defined as receipt of a Paxlovid prescription within 5 days of the patient's COVID-19 index date (positive test or diagnosis). To emulate randomization, we used the clone-censor-weight technique with inverse probability of censoring weights to balance a set of covariates including sex, age, race and ethnicity, comorbidities, community well-being index (CWBI), prior healthcare utilization, month of COVID-19 index, and site of care provision. The primary outcome was hospitalization; death was a secondary outcome. We estimated that Paxlovid reduced the risk of hospitalization by 39% (95% confidence interval (CI) [36%, 41%]; p < 0.001), with an absolute risk reduction of 0.9 percentage points (95% CI [0.9, 1.0]; p < 0.001), and reduced the risk of death by 61% (95% CI [55%, 67%]; p < 0.001), with an absolute risk reduction of 0.2 percentage points (95% CI [0.1, 0.2]; p < 0.001). We also conducted stratified analyses by vaccination status and age group. Absolute risk reduction for hospitalization was similar among patients that were vaccinated and unvaccinate, but was much greater among patients aged 65+ years than among younger patients. We observed disparities in Paxlovid treatment, with lower rates among black and Hispanic or Latino patients, and within socially vulnerable communities. This study's main limitation is that it estimates causal effects using observational data and could be biased by unmeasured confounding.

conclusionsIn this study of Paxlovid's real-world effectiveness, we observed that Paxlovid is effective at preventing hospitalization and death, including among vaccinated patients, and particularly among older patients. This remains true in the era of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Omicron subvariants. However, disparities in Paxlovid treatment rates imply that the benefit of Paxlovid's effectiveness is not equitably distributed.

Indexed as

Antiviral AgentsCOVID-19 Drug TreatmentHospitalizationRitonavirAdultAgedCOVID-19Drug CombinationsElectronic Health RecordsFemaleHumansMaleMiddle AgedSARS-CoV-2Treatment OutcomeUnited StatesAntiviral AgentsDrug CombinationsRitonavir

Identifiers

PMID39823513
PMCPMC11790232

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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.