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Assessment of Optimal Inhaler Therapy Strategies in the Management of Acute Exacerbations of Chronic Obstructive Pulmonary Disease (A.E.C.O.P.D.s) Using Oscillometry: An Observational, Cross-sectional Study

2025· article· en· W4410275480 on OpenAlexaff
Alexandre Siou, Juliano Colapelle, David‐Dan Nguyen, Seyed Massood Nabavi, Olivia C. Iorio, Bryan Ross

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsMcGill UniversityMcGill University Health Centre
Fundersnot available
KeywordsMedicineObservational studyInhalerPulmonary diseaseIntensive care medicineCross-sectional studyCOPDPhysical therapyInternal medicineEmergency medicineAsthmaPathology

Abstract

fetched live from OpenAlex

Abstract Rationale: Chronic obstructive pulmonary disease (COPD) is a lung condition characterized by progressive lung function decline, by chronic respiratory symptoms, as well as by acute exacerbations of COPD (AECOPDs) which are associated with airway inflammation and expiratory airflow limitation. Long-acting bronchodilators (LABDs) are used for daily (chronic) COPD management, while short-acting bronchodilators (SABDs) are typically used during acute exacerbations. Remarkably there remains no standard inhaler approach during exacerbations, and no clear evidence exists on whether LABDs should be continued (to maximize bronchodilation) or withheld (to avoid ‘receptor over-saturation’ and/or side effects) while SABDs are prescribed. This study aimed to evaluate the effectiveness of treating AECOPDs with SABDs alone versus a combination of SABDs and LABDs using oscillometry, a non-invasive method capable of detecting subtle changes in lung mechanics during exacerbations. Methods: This observational, cross-sectional study recruited inpatients with COPD being treated for an exacerbation with both short- and long-acting inhalers by their clinical team. Oscillometry-obtained markers of expiratory flow limitation, the mean inspiratory minus expiratory total respiratory system reactance (mean ΔXrs) and the reactance-volume loop area (AXV), were measured at three distinct time points: 1) at baseline (no bronchodilation), 2) after SABD alone, and 3) after LABD (and within the time window of SABD effect – see Figure). Dyspnea scores were collected using the validated Visual Analog Scale (VAS) at each time point. Unadjusted and adjusted (for age, sex and FEV1) repeated measures ANOVA (RM ANOVA) were used to assess for inter-set differences. Results: Among the first 10 of 37 total planned participants (ClinicalTrials.govNCT06495047), 8 (80%) were female, with a mean age of 70.2±6.7 years and mean FEV1 percent predicted of 33.9±14.9%. There was an average decrease of 0.62±1.0 hPa·s·L−1 in mean ΔXrs, and of 0.1±0.38 hPa·s in AXV, from baseline to combined SABD/LABD administration. Adjusted RM ANOVA demonstrated significant inter-set changes in mean ΔXrs and VAS scores (p=0.003 and p<0.001, respectively). Bonferroni post-hoc analysis revealed significant differences in mean ΔXrs measurements between baseline and combined SABD/LABD administration (i.e. between the first and third set of tests) (p=0.017). No side effects from combined therapies were observed on post-hospitalization chart review. Conclusion: Administration of both SABD and LABD during exacerbations of COPD resulted in significant improvements in expiratory flow limitation and dyspnea scores. Specifically, improvements in expiratory flow limitation were observed following combined therapy (in contrast to short-acting therapy alone) when compared to baseline.

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 score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.064
GPT teacher head0.421
Teacher spread0.357 · 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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