BNP and NT-proBNP Predict Echocardiographic Severity of Diastolic Dysfunction
Bibliographic record
Abstract
AIMS: To evaluate the best combination of clinical parameters and brain natriuretic peptide (BNP) or N-terminal pro-BNP (NT-proBNP), to predict diastolic dysfunction (DD) in heart failure with preserved left ventricular ejection fraction (HF-PLEF) as determined by Doppler-echocardiography. METHODS AND RESULTS: HF patients with EF >40% in the CHARM Echocardiographic Substudy were included and classified to have normal diastolic function, or mild, moderate or severe diastolic dysfunction. Plasma BNP and NT-proBNP levels were measured and relevant clinical characteristics recorded. 181 participants were included in this analysis, 72 (40%) had moderate to severe DD. A model including age, sex, BNP, body mass index, history of atrial fibrillation, coronary artery disease, diabetes mellitus, hypertension and left atrial volume was highly predictive of moderate to severe DD; AUC 0.81 (0.73-0.88; p<0.0001). Similarly, substitution of BNP with NT-proBNP resulted in an AUC 0.79 (0.72-0.87; p<0.0001). In these models; BNP>100 pg/ml (OR 6.24 CI 2.42-16.09, p=0.0002), history of diabetes (OR 3.52 CI 1.43-8.70, p=0.006) and NT-proBNP >600 pg/ml (OR 5.93 CI 2.21-15.92, p=0.0004), history of diabetes mellitus (OR 2.75 CI 1.12-6.76, p=0.03) respectively remained independent predictors of DD in HF-PLEF. CONCLUSIONS: Natriuretic peptides were the strongest independent predictors of DD, as determined by Doppler-echocardiography, in HF-PLEF.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".