Evidence map›Paper›PMID 36894199›Full record

ArticleBMJ open2023

Individualised risk prediction model for exacerbations in patients with severe asthma: protocol for a multicentre real-world risk modelling study.

Tae Yoon Lee, Mohsen Sadatsafavi, Chandra Prakash Yadav, David B Price, Richard Beasley, Christer Janson, Mariko Siyue Koh, Rupsa Roy, Wenjia Chen

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.1field-weighted citation impact, top 25% 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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Review
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  4. 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

9 authors at 5 institutions in 5 countries.

Tae Yoon LeeSaw Swee Hock School of Public Health, National University of Singapore, Singapore.ORCID 0000-0001-6672-4317
Mohsen SadatsafaviRespiratory Evaluation Sciences Program, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia, Canada.
Chandra Prakash YadavSaw Swee Hock School of Public Health, National University of Singapore, Singapore.
David B PriceObservational and Pragmatic Research Institute, Singapore.ORCID 0000-0002-9728-9992
Richard BeasleyMedical Research Institute of New Zealand, Wellington, New Zealand.ORCID 0000-0003-0337-406X
Christer JansonDepartment of Medical Sciences: Respiratory Medicine, Uppsala University, Uppsala, Sweden.ORCID 0000-0001-5093-6980
Mariko Siyue KohDepartment of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore.
Rupsa RoySaw Swee Hock School of Public Health, National University of Singapore, Singapore.
Wenjia ChenSaw Swee Hock School of Public Health, National University of Singapore, Singapore wenjiach@nus.edu.sg.ORCID 0000-0001-8201-7145
National University of Singapore · SGUniversity of British Columbia · CAMedical Research Institute of New Zealand · NZUniversity of Aberdeen · GBUppsala University · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSevere asthma is associated with a disproportionally high disease burden, including the risk of severe exacerbations. Accurate prediction of the risk of severe exacerbations may enable clinicians to tailor treatment plans to an individual patient. This study aims to develop and validate a novel risk prediction model for severe exacerbations in patients with severe asthma, and to examine the potential clinical utility of this tool. METHODS AND ANALYSIS: The target population is patients aged 18 years or older with severe asthma. Based on the data from the International Severe Asthma Registry (n=8925), a prediction model will be developed using a penalised, zero-inflated count model that predicts the rate or risk of exacerbation in the next 12 months. The risk prediction tool will be externally validated among patients with physician-assessed severe asthma in an international observational cohort, the NOVEL observational longiTudinal studY (n=1652). Validation will include examining model calibration (ie, the agreement between observed and predicted rates), model discrimination (ie, the extent to which the model can distinguish between high-risk and low-risk individuals) and the clinical utility at a range of risk thresholds. ETHICS AND DISSEMINATION: This study has obtained ethics approval from the Institutional Review Board of National University of Singapore (NUS-IRB-2021-877), the Anonymised Data Ethics and Protocol Transparency Committee (ADEPT1924) and the University of British Columbia (H22-01737). Results will be published in an international peer-reviewed journal. TRIAL REGISTRATION NUMBER: European Union electronic Register of Post-Authorisation Studies, EU PAS Register (EUPAS46088).

Indexed as

AsthmaCost of IllnessHumansLongitudinal StudiesMulticenter Studies as Topicasthmapreventive medicinerisk management

Identifiers

PMID36894199
PMCPMC10008482
OpenAlexW4323669817

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.