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Record W4220978609 · doi:10.1016/j.jaip.2022.02.047

Short-Acting Beta-2-Agonist Exposure and Severe Asthma Exacerbations: SABINA Findings From Europe and North America

2022· article· en· W4220978609 on OpenAlexaffabout
Jennifer K Quint, Sofie Arnetorp, Janwillem Kocks, Maciej Kupczyk, Javier Nuevo, Vicente Plaza, Claudia Cabrera, Chantal Rahérison, Brandie Walker, Erika Penz, Ileen Gilbert, Njira Lugogo, Ralf J.P. van der Valk, Andrew Fong, Christina Qian, C. Fabry-Vendrand, Chantal Touboul, Dorota Brzostek, Ekaterina Maslova, Filip Surmont, Helena Goike, Hitesh Gandhi, Joke C. Korevaar, Joseph Tkacz, Karissa Johnston, Keith Peres Da Costa, Liset van Dijk, Marcia Vervloet, Michael Pollack, Paul Hernandez, Silvia Boarino, Stephen G. Noorduyn, Wendy Beekman-Hendricks, Yvette M. Weesie

Bibliographic record

VenueThe Journal of Allergy and Clinical Immunology In Practice · 2022
Typearticle
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsUniversity of SaskatchewanUniversity of Calgary
FundersSanofi GenzymeGenentechCovis PharmaAmgenBoehringer IngelheimAstraZenecaChiesi FarmaceuticiMeso Scale DiagnosticsSanofiRegeneron PharmaceuticalsTeva Pharmaceutical IndustriesNovartisGlaxoSmithKline
KeywordsMedicineRate ratioAsthmaDemographyIncidence (geometry)ConcomitantPopulationObservational studyMedical prescriptionFamily medicineInternal medicinePediatricsEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Expert national/global asthma management recommendations raise the issue whether a safe threshold of short-acting beta-2 agonist (SABA) use without concomitant inhaled corticosteroids (ICS) exists. OBJECTIVE: To examine SABA and maintenance therapy associations with severe asthma exacerbations across North America and Europe. METHODS: Observational analyses of 10 SABa use IN Asthma (SABINA) datasets involving 1,033,564 patients (≥12 y) from Canada, France, the Netherlands, Poland, Spain, the United Kingdom, and the United States. Negative binomial models (incidence rate ratio [IRR] [95% CI adjusted for prespecified-covariates]) evaluated associations between SABA and exacerbations. RESULTS: Across severities, 40.2% of patients were prescribed/possessed 3 or more SABA canisters/y. Per the Global Initiative for Asthma (GINA) 2018 definitions, steps 3 to 5-treated patients prescribed/possessing 3 or more versus 1 or 2 SABAs experienced more severe exacerbations (IRR 1.08 [95% CI 1.04‒1.13], U.S. Medicare; IRR 2.11 [95% CI 1.96‒2.27], Poland). This association was not observed in all step 1 or 2-treated patients (the Netherlands, IRR 1.25 [95% CI 0.91‒1.71]; U.S. commercial, IRR 0.92 [95% CI 0.91‒0.93]; U.S. Medicare, IRR 0.74 [95% CI 0.71‒0.76]). We hypothesize that this inverse association between SABA and severe exacerbations in the U.S. datasets was attributable to the large patient population possessing fewer than 3 SABA and no maintenance therapy and receiving oral corticosteroid bursts without face-to-face health care provider encounters. In U.S. SABA monotherapy-treated patients, 3 or more SABAs were associated with more emergency/outpatient visits and hospitalizations (IRR 1.31 [95% CI 1.29‒1.34]). Most GINA 2 to 5-treated study patients (60.6%) did not have maintenance therapy for up to 50% of the time; however, the association of 3 or more SABAs and severe exacerbations persisted (IRR 1.32 [95% CI 1.18‒1.49]) after excluding these patients and the independent effect was further confirmed when U.K. SABA data were analyzed as a continuous variable in patients with up to 100% annual coverage for ICS-containing medications. CONCLUSIONS: Increasing SABA exposure is associated with severe exacerbation risk, independent of maintenance therapy. As addressed by GINA, based on studies across asthma severities where as-needed fast-acting bronchodilators with concomitant ICS decrease severe exacerbations compared with SABA, our findings highlight the importance of avoiding a rescue/reliever paradigm utilizing SABA monotherapy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.431
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.321
Teacher spread0.292 · 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 teacher head, 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

Citations79
Published2022
Admission routes2
Has abstractyes

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