Oral add-on therapies and blood eosinophils in severe acute exacerbations of Chronic Obstructive Pulmonary Disease: A real-world hospital chart review
Bibliographic record
Abstract
RATIONALE Patients may experience acute exacerbation(s) of chronic obstructive pulmonary disease (AECOPD) despite optimal inhaled therapy. Little is known about the prevalence of add-on oral therapies in routine clinical practice. The proportion of subjects who could be candidates for the medications currently under study treating type 2 inflammation is also uncertain.OBJECTIVES The main goal of this review was to review prophylactic azithromycin and roflumilast use in a real-world hospital setting. The secondary objectives were to determine the percentage of AECOPD patients with blood eosinophil counts (BEC) ≥0.3 × 109/L and to identify if BEC were predictive of readmission in individuals on inhaled long-acting muscarinic antagonist (LAMA)/long-acting beta-agonist (LABA)/inhaled corticosteroid (ICS).METHODS Subjects treated in 2022–2023 at Montfort Hospital for AECOPD were reviewed. Medication lists were charted in addition to patient characteristics and bloodwork. AECOPD up to 3 months post-discharge were recorded. Patients on inhaled LAMA/LABA/ICS were compared with univariate and multivariate analyses based on readmission for AECOPD.MEASUREMENTS AND MAIN RESULTS 348 patients were treated for AECOPD. 175 (50%) were already using inhaled LAMA/LABA/ICS. There were 36 subjects on azithromycin (10% overall and 19% of individuals on inhaled triple therapy); no one was prescribed roflumilast. 121 patients (35%) had charted BEC ≥ 0.3 × 109/L in the prior year; 57 (33%) on inhaled LAMA/LABA/ICS. A total of 76 individuals (23%) were readmitted for AECOPD. On multivariate analysis, BEC were not predictive of recurrent AECOPD in subjects prescribed inhaled LAMA/LABA/ICS.CONCLUSIONS This review confirmed the underutilization of oral therapies in subjects who experience a severe AECOPD. Approximately one-third of patients were found to have BEC ≥ 0.3 × 109/L.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".