Association between COPD exacerbations and lung function decline during maintenance therapy
Bibliographic record
Abstract
Background Little is known about the impact of exacerbations on COPD progression or whether inhaled corticosteroid (ICS) use and blood eosinophil count (BEC) affect progression. We aimed to assess this in a prospective observational study. Methods The study population included patients with mild to moderate COPD, aged ≥35 years, with a smoking history, who were followed up for ≥3 years from first to last spirometry recording using two large UK electronic medical record databases: Clinical Practice Research Datalink (CPRD) and Optimum Patient Care Research Database (OPCRD). Multilevel mixed-effects linear regression models were used to determine the relationship between annual exacerbation rate following initiation of therapy (ICS vs non-ICS) and FEV 1 decline. Effect modification by blood eosinophils was studied through interaction terms. Results Of 12178 patients included (mean age 66 years; 48% female), 8981 (74%) received ICS. In patients with BEC ≥350 cells/µL not on ICS, each exacerbation was associated with subsequent acceleration of FEV 1 decline of 19.4 mL/year (95% CI 12.0 to 26.7, p<0.0001). This excess decline was reduced by 15.1 mL/year (6.6 to 23.6) to 4.3 mL/year (1.9 to 6.7, p<0.0001) in those with BEC ≥350 cells/µL treated with ICS. Conclusion Exacerbations are associated with a more rapid loss of lung function among COPD patients with elevated blood eosinophils, defined as ≥350 cells/µL, not treated with ICS. More aggressive prevention of exacerbations using ICS in such patients may prevent excess loss of lung function.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| 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".