Abstract 569: NT-proBNP is an Important Independent Predictor of Clinical Events after Primary PCI for STEMI
Bibliographic record
Abstract
Acute myocardial infarction with ST-segment elevation (STEMI) remains a major global public health issue. Despite advances in therapy, patients remain at risk for death, repeat myocardial infarction (MI) shock and heart failure (HF). Novel markers that predict those at risk are needed. We studied 903 STEMI patients in The Assessment of Pexelizumab in Acute Myocardial Infarction trial (which enrolled STEMI patients presenting < 6 hrs of symptom onset who were to undergo primary PCI) in a case-control design (cases selected based on the trial’s primary composite outcome - death, shock or HF - and matched on age, gender and infarct location to controls). NT-proBNP (pg/ml) was measured at randomization and 24 hrs. Outcomes (individually and the composite) of death, shock, and HF at 90 days were examined by quartiles of NT-proBNP. A CART model was used to categorize adjusted risk. NT-proBNP was higher in patients who had events. Patients with higher NT-proBNP levels at baseline (median symptom onset to randomization 2.7 hrs) and 24 hrs had more events (composite p<0.001; death p<0.0001; HF p<0.0001; shock p=0.05) - See figure . Using the CART model (adjusted for age, gender and infarct location), baseline Killip class and NT-proBNP could further subcategorize patients into 90 day mortality categories 4%, 10%, 30%, and 53%. In fact, only 4 patients (1%) with a 24 hour NT-proBNP <999 pg/ml had any event in the next 90 days. Although the overall prognosis in STEMI patients undergoing primary PCI is good, NT-proBNP performed early and at 24 hrs provides important prognostic information for predicting negative outcomes i.e. shock, heart failure and death. Figure. Kaplan-Meier curve for the primary composite outcome stratified by baseline NT-proBNP (a), or 24 hour NT-proBNP (b).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.004 | 0.001 |
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".