Trial reportThe Journal of allergy and clinical immunology2014
Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data.
Trial report in The Journal of allergy and clinical immunology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04888910 (Novel Inflammatory Markers in Different Phenotypes of Severe Asthma), which is not on this map. Cited by 140 papers.
What it found
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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.
Novel Inflammatory Markers in Different Phenotypes of Severe Asthma
Who cites it
140 citing papers in PubMed.
- Dupilumab Efficacy in Patients with Type 2 Asthma with and without Elevated Blood Neutrophils.Journal of immunology research · 2023Trial
- Biomarker Profiles in Asthma With High vs Low Airway Reversibility and Poor Disease Control.Chest · 2015Trial
- Clinical applications of artificial intelligence-driven nitric oxide: a bibliometric and scientific mapping analysis.Medical gas research · 2026Review
- Machine learning-based MPO and Lp-PLA2 profiling reveals asthma-predominant inflammatory signature in asthma-COPD overlap.The World Allergy Organization journal · 2026Article
- Fueling the fire: metabolic dysfunction and senescence as drivers of lung aging and disease.Physiological reviews · 2026Review
- Context-specific genetic effects inform endotypes and treatment in asthma.The Journal of allergy and clinical immunology · 2026Article
- Early initiation of inhaled corticosteroid-long-acting βPediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2026Article
- NLRP3-mediated epithelial pyroptosis involved in airway hyperresponsiveness in combined eosinophilic and neutrophilic asthma.Cell communication and signaling : CCS · 2026Article
- Sex Differences in Asthma Pathogenesis.Immunological reviews · 2026Review
- Sex, hormones, and lung health.Physiological reviews · 2026Review
- Sex Differences in Lung B Cell Responses in a Murine Model of Asthma.International archives of allergy and immunology · 2026Article
- Leveraging artificial intelligence for the management of preschool wheeze: A narrative review.Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology · 2025Review
- Detection and prediction of real-world severe asthma phenotypes by application of machine learning to electronic health records.The journal of allergy and clinical immunology. Global · 2025Article
- Multicohort Analysis of Bronchial Epithelial Cell Expression in Healthy Subjects and Patients with Asthma Reveals Four Clinically Distinct Clusters.American journal of respiratory cell and molecular biology · 2025Article
- PI3K pathway activation in severe asthma is linked to steroid insensitivity and adverse outcomes.The journal of allergy and clinical immunology. Global · 2025Article
- All-cause and cause-specific mortality in respiratory symptom clusters: a population-based multicohort study.Respiratory research · 2025Article
- Exploring the Asthma - Obesity Link Using Advanced Imaging Techniques.Physiological research · 2025Review
- Innate Immunity and Asthma Exacerbations: Insights From Human Models.Immunological reviews · 2025Review
- Exploring precision treatments in immune-mediated inflammatory diseases: Harnessing the infinite potential of nucleic acid delivery.Exploration (Beijing, China) · 2025Review
- Anti-IL-5 treatment, but not neutrophil interference, attenuates inflammation in a mixed granulocytic asthma mouse model, elicited by air pollution.Respiratory research · 2025Article
80 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundPrevious studies have identified asthma phenotypes based on small numbers of clinical, physiologic, or inflammatory characteristics. However, no studies have used a wide range of variables using machine learning approaches.
objectivesWe sought to identify subphenotypes of asthma by using blood, bronchoscopic, exhaled nitric oxide, and clinical data from the Severe Asthma Research Program with unsupervised clustering and then characterize them by using supervised learning approaches.
methodsUnsupervised clustering approaches were applied to 112 clinical, physiologic, and inflammatory variables from 378 subjects. Variable selection and supervised learning techniques were used to select relevant and nonredundant variables and address their predictive values, as well as the predictive value of the full variable set.
resultsTen variable clusters and 6 subject clusters were identified, which differed and overlapped with previous clusters. Patients with traditionally defined severe asthma were distributed through subject clusters 3 to 6. Cluster 4 identified patients with early-onset allergic asthma with low lung function and eosinophilic inflammation. Patients with later-onset, mostly severe asthma with nasal polyps and eosinophilia characterized cluster 5. Cluster 6 asthmatic patients manifested persistent inflammation in blood and bronchoalveolar lavage fluid and exacerbations despite high systemic corticosteroid use and side effects. Age of asthma onset, quality of life, symptoms, medications, and health care use were some of the 51 nonredundant variables distinguishing subject clusters. These 51 variables classified test cases with 88% accuracy compared with 93% accuracy with all 112 variables.
conclusionThe unsupervised machine learning approaches used here provide unique insights into disease, confirming other approaches while revealing novel additional phenotypes.
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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.