Timing of Death and Myocardial Infarction in Patients with Non‐ST Elevation Acute Coronary Syndromes: Insights From Randomized Clinical Trials
Bibliographic record
Abstract
BACKGROUND: Adverse events occur following non-ST elevation acute coronary syndromes (NSTE ACS). However, the timing of these events in relation to index event is less clear. METHODS: Accordingly, we evaluated 26,466 NSTE ACS patients from the Global Use of Strategies to Open Occluded Arteries in Acute Coronary Syndromes (GUSTO-IIb), Platelet Glycoprotein IIb/IIIa in Unstable Angina: Receptor Suppression Using Integrilin Therapy (PURSUIT), and Platelet IIb/IIIa Antagonism for the Reduction of Acute Coronary Syndrome Events in a Global Organization Network (PARAGON) A and B trials to ascertain the timing of adverse events. Outcomes of interest were death, myocardial infarction (MI), and death or MI at 180 days. Logistic regression modeling for death was used to categorize patients into low-, medium-, and high-risk groups. RESULTS: At 6 months, 6.2% of patients died, 12.1% had MI, and 15.7% suffered death or MI. From 15% to 40% of these events occurred beyond 30 days. At 6 months, 3%, 4%, and 13% of patients died in low-, medium-, and high-risk groups, respectively. However, the proportion of patients dying beyond 30 days was similar in the three groups (44%, 43%, and 41% of death, respectively). Similarly, whereas death or MI increased with higher risk (11%, 14%, and 23%, respectively), the proportion of patients with this event beyond 30 days did not differ in the three strata (22%, 20%, and 25%, respectively). CONCLUSIONS: Our study provides important insights into the timing of adverse events and suggests that the substantial proportion of patients suffer subsequent adverse events after their index NSTE ACS. Thus, these data call for continuous surveillance for these events and efforts beyond the acute phase at increasing adherence to evidence-based therapies to improve the outcomes of these patients.
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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 | 0.026 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".