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Record W7115814746

Ventilation Phenotypes of Severe Asthma

2023· dissertation· en· W7115814746 on OpenAlexaff

Bibliographic record

VenueMacSphere (McMaster University) · 2023
Typedissertation
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVentilation (architecture)Mechanical ventilationSputumAsthmaPhenotypeLung
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION: Abnormal ventilation is the functional consequence of airway obstruction. In patients with severe asthma, ventilation patterns visualized by magnetic resonance imaging (MRI) exhibit significant inter-patient heterogeneity. Therefore, our objectives were to identify MRI ventilation phenotypes of severe asthma using an unsupervised clustering approach and examine their associated demographic, clinical, physiologic, and inflammatory characteristics. METHODS: This retrospective analysis included 58 adults with severe asthma who underwent hyperpolarized 129Xe ventilation MRI. Nineteen quantitative variables were extracted from ventilation MRI (including ventilation defect percent (VDP), ventilation defect size, and ventilation texture features) and transformed to principal components for hierarchical clustering. Differences in demographics, clinical characteristics, spirometry, inflammatory biomarkers, and computed tomography (CT) measurements between phenotypes were evaluated using one-way ANOVA or Kruskal-Wallis tests. RESULTS: Three ventilation phenotypes of severe asthma were identified. They were significantly different with respect to their age, prevalence of obesity, spirometry, sputum neutrophil percent, sputum cytokines (interleukin-4, interleukin-6, interleukin-15, B-cell activating factor), total lung capacity, CT air-trapping, and CT mucus score (all p<0.05). They were not different with respect to their asthma control or medication requirement, and ~75% of each phenotype reported uncontrolled asthma (ACQ-5≥1.5). Phenotype 1 had normal ventilation (VDP=1.7±0.9%) and predominantly consisted of young, obese females (88% female, 41±11 years old, 63% obese). They had normal-to-moderately reduced FEV1 (80±15%pred), normal post-bronchodilator FEV1/FVC, and reduced total lung capacity (85%pred [57-108]). 25% had intraluminal inflammation (all eosinophilic) and their sputum interleukin-4 levels were elevated. Phenotype 2 had markedly abnormal ventilation (VDP=6.2±3.8%) and was older than Phenotype 1, but also predominantly consisted of obese females (63% female, 54±13 years old, 59% obese). They had mildly-to-severely reduced FEV1 (61±17%pred) and partially reversible obstructive spirometry (72%, post-bronchodilator FEV1/FVC<0.70). 50% had intraluminal inflammation (28% eosinophilic/13% neutrophilic/9% mixed-granulocytic) and their sputum interleukin-6 levels were elevated. Phenotype 3 had severely abnormal ventilation (VDP=24.8±10.2%) and was also older than Phenotype 1 but was gender-balanced and not obese (50% female, 56±12 years old, 11% obese). They had moderately-to-very severely reduced FEV1 (41±12%pred) and partially reversible obstructive spirometry (89%, post-bronchodilator FEV1/FVC<0.70). 73% had intraluminal inflammation (39% eosinophilic/17% neutrophilic/17% mixed-granulocytic) and their sputum interleukin-15 and B-cell activating factor levels were elevated. They had the highest burden of gas-trapping and mucus on CT. CONCLUSION: Three distinct MRI ventilation phenotypes of severe asthma were identified through unbiased analysis, all of which reported uncontrolled asthma. The discordance in ventilation between phenotypes, and their characteristics, suggest different mechanisms that may be driving severe asthma.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.234
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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