Sputum Microbiome Is Associated with 1-Year Mortality after Chronic Obstructive Pulmonary Disease Hospitalizations
Bibliographic record
Abstract
Abstract Rationale Lung dysbiosis promotes airway inflammation and decreased local immunity, potentially playing a role in the pathogenesis of acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Objectives We sought to determine the relationship between sputum microbiome at the time of AECOPD hospitalization and 1-year mortality in a COPD cohort. Methods We used sputum samples from 102 patients hospitalized because of AECOPD. All subjects were followed for 1 year after discharge. The microbiome profile was assessed through sequencing of 16S rRNA gene. Microbiome analyses were performed according to 1-year mortality status. To investigate the effect of α-diversity measures and taxon features on time to death, we applied Cox proportional hazards regression models and obtained hazard ratios (HRs) associated with these variables. Measurements and Main Results We observed significantly lower values of α-diversity (richness, Shannon index, evenness, and Faith’s Phylogenetic Diversity) among nonsurvivors (n = 19, 18.6%) than survivors (n = 83, 81.4%). β-Diversity analysis also demonstrated significant differences between both groups (adjusted permutational multivariate ANOVA, P = 0.010). The survivors had a higher relative abundance of Veillonella; in contrast, nonsurvivors had a higher abundance of Staphylococcus. The adjusted HRs for 1-year mortality increased significantly with decreasing α-diversity. We also observed lower survival among patients in whom sputum samples were negative for Veillonella (HR, 13.5; 95% confidence interval, 4.2–43.9; P < 0.001) or positive for Staphylococcus (HR, 7.3; 95% confidence interval, 1.6–33.2; P = 0.01). Conclusions The microbiome profile of sputum in AECOPD is associated with 1-year mortality and may be used to identify subjects with a poor prognosis at the time of hospitalization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.007 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".