Predictive Value of NT-proBNP in Vascular Surgery Patients with COPD and Normal Left Ventricular Systolic Function
Bibliographic record
Abstract
N-terminal pro-B-type natriuretic peptide (NT-proBNP) is commonly used to identify a cardiac cause of dyspnoea. However, patients with chronic obstructive pulmonary disease (COPD) may also have increased plasma NT-proBNP levels because of right-sided myocardial stress caused by pulmonary hypertension. We investigated the relationship between COPD and elevated NT-proBNP levels as well as the impact of elevated NT-proBNP levels on mortality in vascular surgery patients with normal left ventricular systolic function. Prior to vascular surgery, NT-proBNP levels, pulmonary function and left ventricular ejection fraction (LVEF) were assessed in 376 patients. Only patients with a LVEF > 40% were included; n = 261. Elevated NT-proBNP levels were defined as > or =500 pg/ml. Firstly, we assessed the relationship between COPD and NT-proBNP levels. Secondly, we investigated the association between elevated NT-proBNP levels and one-year mortality. COPD was independently associated with elevated NT-proBNP levels (OR 3.36, 95%CI 1.30-8.65) with significant associations found for mild and severe COPD. Elevated NT-proBNP levels were associated with increased one-year mortality in patients with (HR 7.73, 95%CI 1.60-37.43) and without COPD (HR 3.44, 95%CI 1.10-10.73). COPD was associated with elevated NT-proBNP levels in patients with a normal LVEF undergoing vascular surgery. Elevated NT-proBNP levels independent of other well-established risk factors were associated with increased one-year mortality. NT-proBNP may be useful biomarker to risk stratify patients with 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 | 0.001 | 0.004 |
| 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".