<i>N</i> -Terminal Pro–Brain Natriuretic Peptide and Other Risk Markers for the Separate Prediction of Mortality and Subsequent Myocardial Infarction in Patients With Unstable Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND: Biochemical markers are useful for prediction of cardiac events in patients with non-ST-segment-elevation acute coronary syndrome (ACS). The associations between N-terminal pro-brain natriuretic peptide (NT-proBNP) and other biochemical and clinical risk indicators, as well as their prognostic value concerning the individual end points of death and myocardial infarction (MI), were elucidated in a large cohort of ACS patients. METHODS AND RESULTS: NT-proBNP, troponin T, and C-reactive protein (CRP) were analyzed in blood samples obtained at a median of 9.5 hours from symptom onset in 6809 of 7800 ACS patients in the Global Utilization of Strategies To Open occluded arteries-IV (GUSTO-IV) trial. Levels of NT-proBNP were correlated independently with age, female gender, low body weight, diabetes, renal dysfunction, history of MI, heart failure, heart rate, ongoing myocardial damage, and time since onset of ischemia. Increasing quartiles of NT-proBNP were related to short- and long-term mortality that reached 1.8%, 3.9%, 7.7%, and 19.2%, (P<0.001), respectively, at 1 year. Levels of troponin T, CRP, heart rate, and creatinine clearance, in addition to ST-segment depression, were also correlated independently with 1-year mortality, but NT-proBNP was the marker with the strongest relation. In contrast, only troponin T, creatinine clearance, and ST-segment depression were independently related to future MI. The combination of NT-proBNP and creatinine clearance provided the best prediction, with a 1-year mortality of 25.7% with both markers in the top quartile vs 0.3% with both markers in the bottom quartile. CONCLUSIONS: The use of NT-proBNP appears to add critical prognostic insight to the assessment of patients with ACS.
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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 | 0.004 |
| 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 | 0.000 | 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".