Predictive Value of Repeated Versus Single N‐Terminal Pro B‐Type Natriuretic Peptide Measurements Early After‐Myocardial Infarction
Bibliographic record
Abstract
BACKGROUND: A single, markedly elevated B-type natriuretic peptide (BNP) serum concentration predicts an increased risk of death after myocardial infarction (MI), though its sensitivity and predictive accuracy are low. We compared the predictive value of a modestly and persistently elevated, versus a single, markedly elevated measurement of N terminal pro-BNP (NT-BNP) early after MI. METHODS AND RESULTS: NT-BNP was measured 2-4, 6-10, and 14-18 weeks after MI. The median age of the 100 patients was 61 years, median left ventricular ejection fraction (LVEF) was 0.40, and 88% were males. Over a median follow-up of 39 months, 10 patients died. The initial median NT-BNP was 802 pg/mL and declined over time (P = 0.002). An initial NT-BNP > or =2,300 pg/mL (upper quintile) was observed in 19 patients and predicted a 3.4-fold higher independent risk of death (P = 0.05), with modest sensitivity (30%) and positive predictive accuracy (16%). A NT-BNP consistently > or =1,200 pg/mL (upper tertile) was observed in 19 patients, and was associated with a 5.7-fold higher independent risk of death (P = 0.01), with a higher sensitivity (50%) and positive predictive accuracy (26%) than a single, markedly elevated NT-BNP measurement. CONCLUSIONS: A moderately and persistently elevated NT-BNP in the early post-MI period was associated with a 5.7-fold higher risk of death, independent of age, LVEF, and functional class. Compared with a single measurement, serial NT-BNP measurements early after MI were more accurate predictors of risk of death.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| 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".