Reperfusion before percutaneous coronary intervention in ST-elevation myocardial infarction patients is associated with lower N-terminal pro-brain natriuretic peptide levels during follow-up, irrespective of pre-treatment with full-dose fibrinolysis
Bibliographic record
Abstract
AIMS: N-terminal pro-brain natriuretic peptide (NT-proBNP) levels predict outcomes in ST-elevation myocardial infarction patients treated with fibrinolysis or primary percutaneous coronary intervention (PCI). However, its role in facilitated PCI has not yet been assessed; it may be a tool to evaluate the lower event rates with primary PCI in ASSENT-4. METHODS AND RESULTS: In ASSENT-4, 1667 patients were randomized to tenecteplase (TNK) followed by PCI or primary PCI alone. Baseline, discharge/Day 7, and 90-day NT-proBNP levels were available for 1008, 971, and 813 patients. Increasing quartiles of baseline NT-proBNP levels were associated with a higher risk of the combined endpoint of death, heart failure, and shock at 90 days and 1-year mortality (P < 0.001). Events were more common with TNK + PCI, regardless of baseline NT-proBNP quartile. When analysing baseline NT-proBNP as a continuous variable, no treatment interaction was observed for the primary endpoint (P = 0.17) or 1-year mortality (P = 0.08). Overall, NT-proBNP levels at Day 7 or 90 were not different between the two treatments. In patients with TIMI 2-3 flow before PCI, NT-proBNP at Day 90 was lower in PCI-only patients (P = 0.01), although no interaction was observed (P = 0.14). In TNK-pre-treated patients without reperfusion (TIMI 0-1) after PCI, NT-proBNP levels at Day 7 or 90 were not significantly higher than in PCI patients. CONCLUSION: Baseline NT-proBNP predicts outcome at 90 days and 1 year in patients undergoing PCI with or without facilitation with TNK. A higher rate of reperfusion in lytic-pre-treated patients did not result in lower NT-proBNP during follow-up. Thus, baseline and subsequent NT-proBNP levels do not explain the lower mortality rate with PCI alone seen in this trial.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".