Short-Acting Beta-Agonists, Antibiotics, Oral Corticosteroids, and the Associated Burden of COPD
Bibliographic record
Abstract
Background: Severe acute exacerbations of COPD (AECOPDs) are key events that drive health care resource use (HCRU) and negatively impact patients' quality of life. Research Question: What is the real-world burden of COPD relative to patients' medication history, specifically, exposure to short-acting beta-agonists (SABAs), antibiotics, and oral corticosteroids (OCSs)? Study Design and Methods: A population-based retrospective cohort study was conducted of patients in Alberta, Canada, identified as having COPD based on administrative health care data (April 1, 2011-March 31, 2019). The risk of severe AECOPDs over 90 days (COPD events resulting in hospitalization or ED visits) and COPD-specific HCRU were studied relative to prior-year SABA, antibiotic, and OCS history. Results: One hundred eighty-eight thousand nine hundred sixty-nine patients identified with COPD were identified (mean ± SD age, 68.8 ± 13.0 years). After controlling for age, sex, calendar year at index, comorbidities at index, and prior severe AECOPDs, patients with frequent SABA, antibiotic, or OCS exposure in a given year showed significantly higher 90-day risks of severe AECOPDs in a positively associated relationship. Patients with the highest SABA exposure (≥ 6 canisters in a given year) showed twice the rate of severe AECOPDs as patients with 1 SABA canister (incidence rate ratio [IRR], 2.06; 95% CI, 2.01-2.11). The 90-day rates of severe AECOPDs were 51% higher for patients with ≥ 6 vs 1 to 2 antibiotic dispensations (IRR, 1.51; 95% CI, 1.48-1.55) and 3% higher for patients with ≥ 6 vs 1 to 5 OCS burst days (IRR, 1.03; 95% CI, 1.00-1.06). Mean annualized rates of hospitalization and ED visits were highest for patients dispensed ≥ 6 (vs fewer) SABA canisters or antibiotics and patients with any OCS burst days in a given year. Interpretation: Histories of frequent or prolonged exposure to SABAs, antibiotics, or OCSs were associated with higher rates of severe AECOPDs and HCRU.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".