Evidence map›Paper›PMID 41164784›Full record

Trial reportOpen forum infectious diseases2025

Longitudinal Patient-Reported Outcome Trajectories in Long COVID: Findings From the STOP-PASC Clinical Trial.

Prasanna Jagannathan, Haley Hedlin, Jane W Liang, Blake Shaw, Evan Maestri, Michelle Lin, P J Utz, Upinder Singh, Linda N Geng, Hector Bonilla

Abstract readClinical Trial
In one paragraph

Trial report in Open forum infectious diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Prasanna JagannathanDepartment of Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0001-6305-758X
Haley HedlinStanford Quantitative Sciences Unit, Stanford, California, USA.ORCID https://orcid.org/0000-0002-0757-7959
Jane W LiangStanford Quantitative Sciences Unit, Stanford, California, USA.ORCID https://orcid.org/0000-0002-2302-3809
Blake ShawStanford Quantitative Sciences Unit, Stanford, California, USA.
Evan MaestriDepartment of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Michelle LinStanford Quantitative Sciences Unit, Stanford, California, USA.ORCID https://orcid.org/0000-0003-2824-0893
P J UtzDepartment of Medicine, Stanford University School of Medicine, Stanford, California, USA.
Upinder SinghDepartment of Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0003-0630-0306
Linda N GengDepartment of Medicine, Stanford University School of Medicine, Stanford, California, USA.ORCID https://orcid.org/0000-0002-9891-5705
Hector BonillaDepartment of Medicine, Stanford University School of Medicine, Stanford, California, USA.

Funding

Stanford Center for Clinical & Translational Education and Research (Spectrum)UL1TR003142 · NCATS · STANFORD UNIVERSITY · PI O'HARA, RUTH M · 2019 to 2023
$45.0M
Spectrum Stanford Center for clinical and Translational Research and EducationUL1TR001085 · NCATS · STANFORD UNIVERSITY · PI CULLEN, MARK RICHARD, GREENBERG, HARRY BERNARD · 2013 to 2017
$37.1M
NCATS NIH HHS UL1 TR001085NCATS NIH HHS UL1 TR003142
6 · The paper itself

Abstract

Background: Long COVID is a heterogeneous post-infectious condition. Although patient-reported outcome (PRO) measures for diagnosis or therapeutic monitoring have been adapted from related complex chronic illnesses, no PRO has been validated specifically in Long COVID. The STOP-PASC randomized, placebo-controlled trial of nirmatrelvir/ritonavir (NMV/r) in adults with Long COVID showed no overall treatment effect. This exploratory analysis aimed to identify distinct symptom trajectories and clinical characteristics associated with improvement or worsening over time. Methods: We performed latent class trajectory modeling (LCTM) on PRO measures-including the Patient Global Impression of Severity (PGIS), Patient Global Impression of Change (PGIC), PROMIS domains, and core symptoms-among 155 randomized participants. Participants were followed for 15 weeks with serial symptom assessments. Trajectory groups were identified using Bayesian Information Criteria and characterized using descriptive statistics and absolute standardized differences. Results: LCTM revealed heterogeneity in symptom trajectories. Two groups emerged for PGIS (improving Conclusions: Distinct PRO trajectories reflect the clinical heterogeneity of Long COVID. NMV/r showed no clear benefit across subgroups. These findings emphasize the need for validated, Long COVID-specific PRO instruments and targeted therapeutic trials tailored to Long COVID subtypes.

Indexed as

Long COVIDPaxlovidTrajectory mapping

Identifiers

PMID41164784
PMCPMC12560753

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Registered trials

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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.