Early Reinfarction After Fibrinolysis
Bibliographic record
Abstract
BACKGROUND: Trials report a 2% to 6% incidence of reinfarction after fibrinolysis for acute myocardial infarction (MI). We combined the Global Utilization of Streptokinase and Tissue plasminogen activator (alteplase) for Occluded coronary arteries (GUSTO I) and Global Use of Strategies To Open occluded coronary arteries (GUSTO III) populations to better define frequency, timing, and clinical predictors of in-hospital reinfarction. METHODS AND RESULTS: In 55 911 patients with ST-segment elevation myocardial infarction (MI) who were receiving fibrinolysis, we compared baseline characteristics and mortality rate by reinfarction incidence and developed multivariable logistic regression models to predict in-hospital reinfarction and composite of death or reinfarction. Reinfarction occurred in 2258 patients (4.3%) a median of 3.8 days after fibrinolysis; rates did not differ between GUSTO I (4.0%) and GUSTO III (4.2%) or by fibrinolytic assignment (streptokinase, 4.1%; alteplase, 4.3%; reteplase, 4.5%; combined streptokinase and alteplase, 4.4%; P=0.55). Advanced age, shorter time to fibrinolysis, non-US enrollment, nonsmoking status, prior MI or angina, female sex, anterior MI, and lower systolic blood pressure were associated significantly with reinfarction. Patients with reinfarction had higher mortality at 30 days (11.3% versus 3.5% without reinfarction; odds ratio, 3.5; P<0.001) and from 30 days to 1 year (4.7% versus 3.2%; hazard ratio, 1.5; P<0.001). Significant multivariate predictors of in-hospital death or reinfarction included age, Killip class, systolic and diastolic blood pressures, heart rate, anterior MI, smoking status, prior MI, sex, and country of enrollment (all P<0.001). CONCLUSIONS: Reinfarction occurs infrequently after fibrinolysis but confers increased risk of 30-day and 1-year mortality. Some predictors of reinfarction differ from known predictors of death after MI. Improved treatment and prevention strategies for reinfarction deserve study.
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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 | 0.005 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".