ArticleChest2025
Prediction Pathway for Severe Asthma Exacerbations: A Bayesian Network Analysis.
Article in Chest, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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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.
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.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of type 2 inflammation-related biomarkers in risk prediction models for acute exacerbations in adult asthma: a systematic review.Frontiers in medicine · 2026Pooled it
- Breaking the bottleneck of asthma treatment: the future of omni-targeted therapy.Journal of advanced research · 2026Review
- Understanding Factors Influencing HIV pre-exposure prophylaxis adherence among men who have sex with men in Western China: a data-driven exploration using bayesian networks.BMC public health · 2025Article
Corrections and comments
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Authors and funding
41 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAccurate risk prediction of exacerbations is pivotal in severe asthma management. Multiple risk factors are at play, but the pathway of risk prediction remains unclear. RESEARCH QUESTION: How do the interplays of clinically relevant predictors lead to severe exacerbations in patients with severe asthma? STUDY DESIGN AND
methodsPatients with severe asthma (n = 6,814, aged ≥ 18 years), biologic naive, were identified from the Severe Asthma Registry (2017-2021). Relevant predictors covered demographics, lung function, inflammation biomarkers, health care use, medications, exacerbation history, and comorbidities. A Bayesian network, representing the prediction process of severe exacerbations, was obtained by combining expert knowledge and machine learning algorithms. Internal validation was performed. The proposed influence diagram integrated decision and utility nodes into the prediction pathway.
resultsThe Bayesian network analysis revealed that blood eosinophil count, fractional exhaled nitric oxide level, and FEV
interpretationThis study identified an essential prediction pathway of severe exacerbation, which involves the influence of chronic rhinosinusitis on the immediate predictors of risk transition from current to future severe asthma exacerbations. Macrolide use was another essential prediction pathway identified. The findings support shared clinical decision-making in severe asthma treatment.
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Registered trials
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.