Impacts of coexisting bronchial asthma on severe exacerbations in mild-to-moderate COPD: results from a national database
Bibliographic record
Abstract
BACKGROUND: Acute exacerbations are major drivers of COPD deterioration. However, limited data are available for the prevalence of severe exacerbations and impact of asthma on severe exacerbations, especially in patients with mild-to-moderate COPD. METHODS: Patients with mild-to-moderate COPD (≥40 years) were extracted from Korean National Health and Nutrition Examination Survey data (2007-2012) and were linked to the national health insurance reimbursement database to obtain medical service utilization records. RESULTS: Of the 2,397 patients with mild-to-moderate COPD, 111 (4.6%) had severe exacerbations over the 6 years (0.012/person-year). Severe exacerbations were more frequent in the COPD patients with concomitant self-reported physician-diagnosed asthma compared with only COPD patients (P<0.001). A multiple logistic regression presented that asthma was an independent risk factor of severe exacerbations in patients with mild-to-moderate COPD regardless of adjustment for all possible confounding factors (adjusted odds ratio, 1.67; 95% confidence interval, 1.002-2.77, P=0.049). In addition, age, female, poor lung function, use of inhalers, and low EuroQoL five dimensions questionnaire index values were independently associated with severe exacerbation in patients with mild-to-moderate COPD. CONCLUSION: In this population-based study, the prevalence of severe exacerbations in patients with mild-to-moderate COPD was relatively low, compared with previous clinical interventional studies. Coexisting asthma significantly impacted the frequency of severe exacerbations in patients with mild-to-moderate COPD, suggesting application of an exacerbation preventive strategy in these patients.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".