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Relationship Between Intra-breath Oscillometry and Pulmonary Function Testing in Patients With COPD: A Cross-sectional Study

2025· article· en· W4410275536 on OpenAlexaff
Sheida Nabavi, A. Siou, Felix‐Antoine Coutu, Dany Malaeb, O. Lorio, Bryan Ross

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicinePulmonary function testingCOPDCross-sectional studyCardiologyInternal medicineIntensive care medicinePathology

Abstract

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Abstract Rationale A complete pulmonary function test (PFT) includes plethysmography and diffusion capacity testing, which are considered the reference measurements for assessing hyperinflation, gas trapping, and lung diffusing capacity. However, conducting full PFTs can be challenging due to the significant time and staff resources required, and can be difficult to access. In contrast, oscillometry is increasingly being used in daily clinical practice as a non-invasive method to characterize the mechanical properties of the respiratory system. Intra-breath oscillometry measurements have emerged as a highly sensitive technique through the detection of patterns which are not reflected by standard spectral analysis. The present study aimed to evaluate the capability of intra-breath oscillometry parameters and their correlations with hyperinflation, gas trapping, and lung diffusion capacity as an alternative to a full PFT. Methods Data from a cross-sectional observational study (ClinicalTrials.govNCT05913323), which included adult participants with spirometry-confirmed “mild” (GOLD 1) or “very severe” (GOLD 4) COPD, was used. Intra-breath oscillometry parameters, i.e., the mean difference in lung reactance between inspiration and expiration cycles (ΔXrs) and reactance-volume loop area (AXV), were calculated from testing performed at a frequency of 10 Hz. Each participant also underwent a full PFT, which included measurements of total lung capacity (TLC) as a marker of hyperinflation, the ratio of residual volume (RV) to TLC (RV/TLC) as a marker of gas trapping, and the Diffusing Capacity of the Lung for Carbon Monoxide (DLco) as a marker of gas transfer. Spearman correlation analysis, adjusted for age, sex, and FEV1, was conducted to estimate the association between the intra-breath parameters and PFT measurements. Results In 17 participants, 9 had mild and 8 had very severe COPD. Of these participants, 52.94% were female, with mean age of 66.4±8.3 years, and mean FEV1 of 1.5L (57.84% predicted). The strongest correlation coefficient observed was between ΔXrs and RV/TLC (0.75 [0.46,0.9] p<0.001), while ΔXrs was inversely correlated with DLco (-0.72 [-0.84,-0.41] p<0.001). AXV showed a weaker correlation with RV/TLC (0.69 [0.38,0.85] p<0.001) and a weaker inverse correlation with DLco (-0.50 [-0.87,-0.01] p<0.001). The correlation coefficients of ΔXrs and AXV with TLC were less than 0.5 (see Figure 1). Conclusions These results confirm that intra-breath oscillometry parameters can potentially be used to assess gas trapping and gas transfer in patients with COPD, serving as a possible complement and/or alternative to full PFTs.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.032
GPT teacher head0.348
Teacher spread0.317 · 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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