Evidence map›Paper›PMID 41513747›Full record

ArticleScientific reports2026

A Bayesian network for identifying causes of breathlessness using a national electronic medical records (EMR) database.

Alamgir Kabir, Anthony Devaux, Christine Jenkins, Allison Martin, Anthony Sunjaya, Christopher Gianacas, David Price, Nevaashni Eleangovan, Anna Campain

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PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. 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

9 authors.

Alamgir KabirThe George Institute for Global Health, University of New South Wales, Sydney, Australia.
Anthony DevauxThe George Institute for Global Health, University of New South Wales, Sydney, Australia. adevaux@georgeinstitute.org.au.
Christine JenkinsThe George Institute for Global Health, University of New South Wales, Sydney, Australia.
Allison MartinThe George Institute for Global Health, University of New South Wales, Sydney, Australia.
Anthony SunjayaThe George Institute for Global Health, University of New South Wales, Sydney, Australia.
Christopher GianacasThe George Institute for Global Health, University of New South Wales, Sydney, Australia.
David PriceObservational and Pragmatic Research Institute, Singapore, Singapore.
Nevaashni EleangovanObservational and Pragmatic Research Institute, Singapore, Singapore.
Anna CampainThe George Institute for Global Health, University of New South Wales, Sydney, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breathlessness is a common symptom linked to various respiratory and cardiac conditions, often causing delays in diagnosis and management in primary care. This study aimed to develop an algorithm using Bayesian Networks (BN) to predict ten conditions associated with presentations of breathlessness. The study included individuals aged 16 years and older with breathlessness recorded as the reason for visiting a general practitioner (GP) between 2002 and 2024 in 50 practices in the United Kingdom (UK). Thirty-four characteristics including demographic, symptoms, comorbidities and medication histories were considered as predictors. A total of 384,994 breathlessness episodes from 136,215 patients were analysed, with 55% being female and 42% aged over 60 years. The BN achieved good performance with area under the curve (ROC-AUC) values ranging from 0.68 for non-pneumonia lower respiratory tract infections (LRTI) to 0.94 for heart failure. The algorithm demonstrated strong predictive performance and could help GPs prioritise early and targeted diagnostic tests for the most likely causes of breathlessness, potentially reducing both diagnostic delays and costs.

Indexed as

AlgorithmsDatasets as TopicDyspneaAdolescentAdultAgedBayes TheoremComorbidityElectronic Health RecordsFemaleHumansMaleMiddle AgedUnited KingdomYoung Adult

Identifiers

PMID41513747
PMCPMC12873358

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.