B-type natriuretic peptides in chronic obstructive pulmonary disease: a systematic review
Bibliographic record
Abstract
BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) have increased cardiovascular risk. Natriuretic peptides (NP) in other populations are useful in identifying cardiovascular disease, stratifying risk, and guiding therapy. METHODS: We performed a systematic literature review to examine NP in COPD, utilising Medline, EMBASE, and the Cochrane Library. RESULTS: Fifty one studies were identified. NP levels were lower in stable compared to exacerbation of COPD, and significantly increased with concomitant left ventricular systolic dysfunction or cor pulmonale. Elevation occurred in 16 to 60% of exacerbations and persisted in approximately one half of patients at discharge. Cardiovascular comorbidities were associated with increased levels. Levels consistently correlated with pulmonary artery pressure and left ventricular ejection fraction, but not pulmonary function or oxygen saturation. NP demonstrated high negative predictive values (0.80 to 0.98) to exclude left ventricular dysfunction in both stable and exacerbation of COPD, but relatively low positive predictive values. NP elevation predicted early adverse outcomes, but the association with long term mortality was inconsistent. CONCLUSION: NP reflect diverse aspects of the cardiopulmonary continuum which limits utility when applied in isolation. Strategies integrating NP with additional variables, biomarkers and imaging require further investigation.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".