Role of B‐Type Natriuretic Peptide and N‐Terminal Prohormone BNP as Predictors of Cardiovascular Morbidity and Mortality in Patients With a Recent Coronary Event and Type 2 Diabetes Mellitus
Bibliographic record
Abstract
Background Natriuretic peptides are recognized as important predictors of cardiovascular events in patients with heart failure, but less is known about their prognostic importance in patients with acute coronary syndrome. We sought to determine whether B‐type natriuretic peptide ( BNP ) and N‐terminal prohormone B‐type natriuretic peptide ( NT ‐pro BNP ) could enhance risk prediction of a broad range of cardiovascular outcomes in patients with acute coronary syndrome and type 2 diabetes mellitus. Methods and Results Patients with a recent acute coronary syndrome and type 2 diabetes mellitus were prospectively enrolled in the ELIXA trial (n=5525, follow‐up time 26 months). Best risk models were constructed from relevant baseline variables with and without BNP / NT ‐pro BNP . C statistics, Net Reclassification Index, and Integrated Discrimination Index were analyzed to estimate the value of adding BNP or NT ‐pro BNP to best risk models. Overall, BNP and NT ‐pro BNP were the most important predictors of all outcomes examined, irrespective of history of heart failure or any prior cardiovascular disease. BNP significantly improved C statistics when added to risk models for each outcome examined, the strongest increments being in death (0.77–0.82, P <0.001), cardiovascular death (0.77–0.83, P <0.001), and heart failure (0.84–0.87, P <0.001). BNP or NT ‐pro BNP alone predicted death as well as all other variables combined (0.77 versus 0.77). Conclusions In patients with a recent acute coronary syndrome and type 2 diabetes mellitus, BNP and NT ‐pro BNP were powerful predictors of cardiovascular outcomes beyond heart failure and death, ie, were also predictive of MI and stroke. Natriuretic peptides added as much predictive information about death as all other conventional variables combined. Clinical Trial Registration URL : http://www.clinicaltrials.gov . Unique identifier: NCT 01147250.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".