High Plasma Brain Natriuretic Peptide Levels in Stable COPD without Pulmonary Hypertension or Cor Pulmonale
Bibliographic record
Abstract
BACKGROUND: Early diagnosis of chronic obstructive pulmonary disease (COPD) with latent pulmonary hypertension (PH) and cor pulmonale is important because the prognosis of this condition is poor. OBJECTIVE: To investigate the utility of brain natriuretic peptide (BNP) for prognostication of COPD, plasma BNP was measured in patients with COPD without symptoms or physical findings of PH or cor pulmonale. METHODS: Plasma BNP was measured in 60 patients with COPD, 10 asthmatics, and 30 healthy subjects. Echocardiography, arterial blood gas analysis, and spirometry were also performed. Mortality and exacerbation were compared between COPD patients with high and low plasma BNP levels over a 3-year follow-up period. RESULTS: Plasma BNP (mean +/- SEM, pg/mL) in COPD patients (41.0+/-6.6) was significantly higher than in normal subjects (14.8+/-2.7) and asthmatics (17.4+/-4.5) (p<0.0001 and p<0.05, respectively). No significant correlations were observed between plasma BNP level and pulmonary function or hypoxia. There was, however, a significant correlation between plasma BNP level and % ejection fraction (r=-0.41, p=0.0197) and pulmonary artery systolic pressure (r=0.5, p=0.004). The period until initial COPD exacerbation in subjects with a high plasma BNP level was significantly shorter (p<0.05). Plasma BNP level during exacerbations (79.9+/-16.2) was also significantly higher than during stable disease (41.2+/-8.7) (p=0.004). CONCLUSION: We suggest that plasma BNP is a non-invasive biomarker that can be used as a screening parameter for latent PH and left ventricular dysfunction, and also as a predictor of exacerbation in stable COPD.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".