Evidence map›Paper›PMID 39622563›Full record

ArticleBMJ open2024

Defining within-host SARS-CoV-2 RNA viral load kinetics during acute COVID-19 infection within different respiratory compartments and their respective associations with host infectiousness: a protocol for a systematic review and meta-analysis.

Daniel Pan, Christopher A Martin, Joshua Nazareth, Shirley Sze, Amani Al-Oraibi, Mayuri Gogoi, Natalia Grolmusova, Pip Divall, Jonathan Decker, Eve Fletcher and 14 more

Abstract read
In one paragraph

Article in BMJ open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

24 authors.

Daniel PanDevelopment Centre for Population Health, University of Leicester, Leicester, UK.
Christopher A MartinDevelopment Centre for Population Health, University of Leicester, Leicester, UK.ORCID http://orcid.org/0000-0002-2337-4799
Joshua NazarethDevelopment Centre for Population Health, University of Leicester, Leicester, UK.ORCID http://orcid.org/0000-0002-0603-9091
Shirley SzeDepartment of Cardiovascular Sciences, University of Leicester, Leicester, UK.
Amani Al-OraibiDevelopment Centre for Population Health, University of Leicester, Leicester, UK.ORCID http://orcid.org/0000-0003-1502-2627
Mayuri GogoiDevelopment Centre for Population Health, University of Leicester, Leicester, UK.ORCID http://orcid.org/0000-0002-9946-2509
Natalia GrolmusovaDepartment of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK.
Pip DivallEducation Centre Library, University Hospitals of Leicester NHS Trust, Leicester, UK.
Jonathan DeckerDepartment of Respiratory Sciences, University of Leicester, Leicester, UK.
Eve FletcherDepartment of Respiratory Sciences, University of Leicester, Leicester, UK.
Caroline WilliamsDepartment of Respiratory Sciences, University of Leicester, Leicester, UK.
James HayOxford Big Data Institute, University of Oxford, Oxford, UK.
Rebecca F BaggaleyDevelopment Centre for Population Health, University of Leicester, Leicester, UK.
Anne L WyllieDepartment of Epidemiology of Microbial Diseases, Yale University, New Haven, Connecticut, USA.
Iain StephensonDepartment of Infectious Diseases and HIV Medicine, University Hospitals of Leicester NHS Trust, Leicester, UK.
Erol GaillardDepartment of Respiratory Sciences, University of Leicester, Leicester, UK.
Laura B NellumsCollege of Population Health, University of New Mexico Health Sciences Center, Albuquerque, New Mexico, USA.
Tristan William ClarkClinical and Experimental Sciences, University of Southampton, Southampton, UK.ORCID http://orcid.org/0000-0001-6026-5295
Jonathan Nguyen Van-TamLifespan and Population Health, University of Nottingham, Nottingham, UK.
Benjamin J CowlingWHO Collaborating Centre for Infectious Disease Epidemiology and Control, Hong Kong School of Public Health, Hong Kong, Hong Kong.
T Déirdre HollingsworthOxford Big Data Institute, University of Oxford, Oxford, UK.
Laura GrayLeicester NIHR Biomedical Research Centre, Leicester, UK.
Michael BarerDepartment of Respiratory Sciences, University of Leicester, Leicester, UK.
Manish PareekDevelopment Centre for Population Health, University of Leicester, Leicester, UK manish.pareek@leicester.ac.uk.ORCID http://orcid.org/0000-0003-1521-9964

Funding

Yale Clinical and Translational Science Award (U Component)UL1TR001863 · NCATS · YALE UNIVERSITY · PI John H. Krystal, LUCILA OHNO-MACHADO · 2016 to 2026
$102.9M
NCATS NIH HHS UL1 TR001863
6 · The paper itself

Abstract

introductionUnderstanding how RNA viral load changes (viral load kinetics) during acute infection in SARS-CoV-2 can help to identify when and which patients are most infectious. We seek to summarise existing data on the longitudinal RNA viral load kinetics of SARS-CoV-2 sampled from different parts of the respiratory tract (nose, nasopharynx, oropharynx, saliva and exhaled breath) and how this may vary with age, sex, ethnicity, immune status, disease severity, vaccination, treatment and virus variant. METHODS AND ANALYSIS: We will conduct a systematic review and meta-analysis, using studies identified through MEDLINE and EMBASE (via Ovid). All research studies reporting primary data on longitudinal RNA viral load kinetics of infected patients with SARS-CoV-2 will be included. Methodological quality will be assessed using a validated checklist for longitudinal studies as well as predefined quality criteria for assessment of individual-level RNA viral load. Should the data allow, we will aim to perform individual patient-level meta-analysis. Our primary outcomes are duration to, and quantity of peak RNA viral load, and total duration of viral load shedding within different respiratory compartments. Secondary outcomes include duration of lateral flow antigen and virus culture positivity and variation of RNA viral load by age, immune status, disease severity, vaccination, treatment, lateral flow tests, viral culture positivity and SARS-CoV-2 variant. Study-level effects affecting observations, but not related to properties of the patient, such as the PCR platform and gene target will also be recorded. Random-effects models will estimate the population mean and individual-level variation in viral shedding conditional on the aforementioned variables. Finally, we will summarise the key mechanistic models used in the literature to reconstruct individual-level viral kinetics and estimate how different factors shape viral dynamics over time. ETHICS AND DISSEMINATION: Ethical approval is not needed as data will be obtained from published articles or studies with data that have already received and ethical review for analysis. Manuscript(s) will be prepared for publication. SYSTEMATIC REVIEW PROTOCOL REGISTRATION: PROSPERO ID: CRD42023385315.

Indexed as

COVID-19Meta-Analysis as TopicRNA, ViralSARS-CoV-2Systematic Reviews as TopicViral LoadHumansKineticsNasopharynxResearch DesignRespiratory SystemRNA, ViralInfection controlINFECTIOUS DISEASESMeta-AnalysisRESPIRATORY MEDICINE (see Thoracic Medicine)Review

Identifiers

PMID39622563
PMCPMC11624818

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.