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Oscillometry and pulmonary MRI measurements of ventilation heterogeneity in obstructive lung disease: relationship to quality of life and disease control

2018· article· en· W2792024375 on OpenAlexafffund
Heather M. Young, Fumin Guo, Rachel L. Eddy, Geoffrey N. Maksym, Grace Párraga

Bibliographic record

VenueJournal of Applied Physiology · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsDalhousie UniversityWestern University
FundersCanadian Institutes of Health ResearchGovernment of Canada
KeywordsMedicineCOPDAsthmaVentilation (architecture)CardiologyAirway resistanceRespiratory systemLungInternal medicinePlethysmographRespiratory diseaseObstructive lung diseaseMagnetic resonance imagingRadiology

Abstract

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Ventilation heterogeneity is a hallmark finding in obstructive lung disease and may be evaluated using a variety of methods, including multiple-breath gas washout and pulmonary imaging. Such methods provide an opportunity to better understand the relationships between structural and functional abnormalities in the lungs, and their relationships with important clinical outcomes. We measured ventilation heterogeneity and respiratory impedance in 100 subjects [50 patients with asthma, 22 ex-smokers, and 28 patients with chronic obstructive pulmonary disease (COPD)] using oscillometry and hyperpolarized 3He magnetic resonance imaging (MRI) and determined their relationships with quality of life scores and disease control/exacerbations. We also coregistered MRI ventilation maps to a computational airway tree model to generate patient-specific respiratory impedance predictions for comparison with experimental measurements. In COPD and asthma patients, respectively, forced oscillation technique (FOT)-derived peripheral resistance (5–19 Hz) and MRI ventilation defect percentage (VDP) were significantly related to quality of life (FOT: COPD ρ = 0.4, P = 0.004; asthma ρ = −0.3, P = 0.04; VDP: COPD ρ = 0.6, P = 0.003; asthma ρ = −0.3, P = 0.04). Patients with poorly controlled asthma (Asthmatic Control Questionnaire >2) had significantly increased resistance (5 Hz: P = 0.01; 5–19 Hz: P = 0.006) and reactance (5 Hz: P = 0.03). FOT-derived peripheral resistance (5–19 Hz) was significantly related to VDP in patients with asthma and COPD patients (asthma: ρ = 0.5, P < 0.001; COPD: ρ = 0.5, P = 0.01), whereas total respiratory impedance was related to VDP only in patients with asthma (resistance 5 Hz: ρ = 0.3, P = 0.02; reactance 5 Hz: ρ = −0.5, P < 0.001). Model-predicted and FOT-measured reactance (5 Hz) were correlated in patients with asthma (ρ = 0.5, P = 0.001), whereas in COPD patients, model-predicted and FOT-measured resistance (5–19 Hz) were correlated (ρ = 0.5, P = 0.004). In summary, in patients with asthma and COPD patients, we observed significant, independent relationships for FOT-measured impedance and MRI ventilation heterogeneity measurements with one another and with quality of life scores. NEW & NOTEWORTHY In 100 patients, including patients with asthma and ex-smokers, 3He MRI ventilation heterogeneity and respiratory system impedance were correlated and both were independently related to quality of life scores and asthma control. These findings demonstrated the critical relationships between respiratory system impedance and ventilation heterogeneity and their role in determining quality of life and disease control. These observations underscore the dominant role that abnormalities in the lung periphery play in ventilation heterogeneity that results in patients’ symptoms.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.038
GPT teacher head0.329
Teacher spread0.290 · 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

Citations59
Published2018
Admission routes2
Has abstractyes

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