Association between inhaled corticosteroids and mortality in critically ill patients with AECOPD: an analysis of two observational cohorts
Bibliographic record
Abstract
Introduction: Inhaled corticosteroids(ICS) improve symptoms and lung function in patients with AECOPD. But whether ICS therapy reduces mortality during severe AECOPD remains unknown. Aims and objectives: To determine whether ICS modifies the risk of death in critically ill patients with AECOPD. Methods: This is a post-hoc analysis. Data of two cohorts were from critically ill patients admitted to ICUs of 209 hospitals in the U.S. These patients who received any corticosteroids by nebulized or inhaler therapy in the ICU were classified as the observation group, whereas those who didn’t receive any ICS were classified as the control group. The primary outcome was the 28-day mortality following ICU admission. The primary analysis included all patients with data available for the primary endpoint and was adjusted for risk measured using a multivariable logistic regression model with inverse probability of treatment weighting according to propensity score. Results: We included 4,555 critically ill patients with AECOPD. In the MIMIC-IV cohort, the primary outcome occurred in 72 of 443 patients(16.3%) in ICS-treated group versus 110 of 386 patients(28.5%) in the control group(adjusted OR, 0.495; 95% confidence interval[CI], 0.330 to 0.743; P＜0.001). In the eICU-CRD cohort, the primary outcome occurred in 6 of 278 patients(2.2%) in ICS-treated group versus 247 of 3,448 patients(7.2%) in the control group(adjusted OR, 0.348; 95% CI, 0.147 to 0.823; P=0.016). These results were robust to different assumptions. Conclusions: ICS treatment was associated with a significantly lower rate of death from any cause within 28 days in critically ill patients with AECOPD.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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".