How can time to COPD exacerbation be delayed? A real-world study comparing two oscillating positive expiratory pressure (OPEP) devices in patients with chronic obstructive pulmonary disease (COPD) or chronic bronchitis
Bibliographic record
Abstract
Rationale: Acute COPD exacerbations are common and a main driver of hospitalizations. This retrospective study compared disease-related hospital readmission in COPD/chronic bronchitis patients using two OPEP devices. Methods: Patients were identified on either the Aerobika (Trudell Medical International) or Acapella (Smiths Medical) OPEP devices from September 2013 to April 2018 in IQVIAs hospital claims data linked to medical (Dx) and prescription claims (LRx); the first COPD/chronic bronchitis hospital visit with an OPEP device being index. Patients were ≥18 years old, had ≥1 hospital, LRx, Dx record within 12 months before and after index, and had no asthma diagnosis before index or post-operative OPEP device use within 30 days before index. Kaplan-Meier survival analysis was used to compare time from discharge to disease-related readmission, and readmission rates were determined at 30 days and 12 months post-discharge for 1:3 propensity score (PS)-matched Aerobika and Acapella users. Results: 619 Aerobika users were matched to 1,857 Acapella users (mean age 72 years). Aerobika users had a significantly longer time to readmission than Acapella users (p=0.01). Readmission rates (proportion of patients having at least one) were lower for Aerobika users at 30 days (11% vs 17%) and 12 months (40% vs 45%). Conclusions: Patients given an Aerobika device compared to an alternative OPEP device had delayed time to readmission. This supports use of the Aerobika* OPEP device as an add-on to usual care post-exacerbation and highlights differences in OPEP devices effectiveness.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".