Evidence mapPaperPMID 37464130Full record

ArticleAdvances in experimental medicine and biology2023

Clinical Trial Design Innovations for Precision Medicine in Asthma.

Salman Siddiqui, Elin Haf Davies, Majid Afshar, Loren C Denlinger

Abstract read
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In one paragraph

Article in Advances in experimental medicine and biology, 2023. 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
13.8field-weighted citation impact, top 1% of its field
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, 3 citations in OpenAlex.

  1. Review
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

4 authors at 2 institutions in 2 countries.

Salman SiddiquiNational Heart and Lung Institute, Imperial College, London, England, UK.
Elin Haf DaviesAparito Limited, Wrexham, Wales, UK.
Majid AfsharDivision of Allergy, Pulmonary and Critical Care, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Loren C DenlingerDivision of Allergy, Pulmonary and Critical Care, Department of Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. lcd@medicine.wisc.edu.
University of Wisconsin–Madison · USImperial College London · GB

Funding

NHLBI NIH HHS U10 HL109086
6 · The paper itself

Abstract

Severe asthma is a spectrum disorder with numerous subsets, many of which are defined by clinical history and a general predisposition for T2 inflammation. Most of the approved therapies for severe asthma have required clinical trial designs with population enrichment for exacerbation frequency and/or elevation of blood eosinophils. Moving beyond this framework will require trial designs that increase efficiency for studying nondominant subsets and continue to improve upon biomarker signatures. In addition to reviewing the current literature on biomarker-informed trials for severe asthma, this chapter will also review the advantages of master protocols and adaptive design methods for establishing the efficacy of new interventions in prospectively defined subsets of patients. The incorporation of methods that allow for data collection outside of traditional study visits at academic centers, called remote decentralized trial design, is a growing trend that may increase diversity in study participation and allow for enhanced resiliency during the COVID-19 pandemic. Finally, reaching the goals of precision medicine in asthma will require increased emphasis on effectiveness studies. Recent advances in real-world data utilization from electronic health records are also discussed with a view toward pragmatic trial designs that could also incorporate the evaluation of biomarker signatures.

Indexed as

AsthmaCOVID-19Precision MedicineBiomarkersClinical Trials as TopicHumansPandemicsBiomarkersBiomarkersCohortEndotypesExacerbationInterventionLongitudinal analysisLung functionPhenotypesPlacebo-controlled trialsRisk estimateStratification

Identifiers

PMID37464130
OpenAlexW4384663676

What Socratic holds

Textmetadata
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 Socratic graph.