Changes in N-Terminal Pro-B-Type Natriuretic Peptide Levels and Outcomes in Heart Failure with Preserved Ejection Fraction: An Analysis of the I-Preserve Study
Bibliographic record
Abstract
AIMS: In patients with heart failure (HF) and reduced ejection fraction, decreases or increases in NT-proBNP levels are associated with better and worse outcomes, respectively. The association in HF and preserved ejection fraction (HF-PEF) is unknown. We examined the association between change in level of NT-proBNP and prognosis in patients with HF-PEF. METHODS AND RESULTS: We examined the association between change in NT-proBNP from baseline to 6 months and cardiovascular (CV) death or HF hospitalization in 2612 participants in the Irbesartan in Patients with Heart Failure and Preserved Systolic Function Study (I-Preserve). Change in NT-proBNP was modelled as a restricted cubic spline in a Cox model after adjusting for baseline NT-proBNP and known prognostic variables. Median change in NT-proBNP from baseline was -7 pg/mL (interquartile range -143 to +108). After adjustment, a 1000 pg/mL decrease in NT-proBNP from baseline was associated with a reduction in the risk of CV death or HF hospitalization [hazard ratio (HR) 0.73, 95% confidence interval (CI) 0.53-1.02]; a 1000 pg/mL increase was associated with an increase in risk (HR 2.01, 95% CI 1.50-2.69). Beyond a 1000 pg/mL rise or fall, there was little additional change in risk. Addition of change in NT-proBNP at 6 months to a model with only baseline NT-proBNP improved the C-statistic from 0.752 to 0.769 (P = 0.013). CONCLUSION: In HF-PEF, a rise in NT-proBNP was associated with an increase in risk of CV death or HF hospitalization and a fall was associated with a trend towards a decrease in risk. NT-proBNP may be a useful marker to monitor prognosis in this condition.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".