Predictors and Prognostic Impact of Recurrent Myocardial Infarction in Patients with Left Ventricular Dysfunction, Heart Failure, or Both Following a First Myocardial Infarction
Bibliographic record
Abstract
UNLABELLED: IMS: Recurrent myocardial infarction (MI) is common after a first MI and is associated with increased morbidity and mortality. Predictors and prognosis of a recurrent MI with contemporary management are not well known. METHODS AND RESULTS: We assessed the predictors and prognostic impact of a first recurrent MI in 10,599 patients with left ventricular dysfunction, heart failure, or both following a first MI from the Valsartan in Acute Myocardial Infarction Trial (VALIANT) cohort. During a median follow-up of 27.4 months, 861 patients (9.6%) had a recurrent MI. The median time to recurrence was 136 days (quartiles 35-361 days), with a declining rate of recurrent MI within the first 3 months. The strongest predictors of recurrent MI were reduced estimated glomerular filtration rate, unstable angina, diabetes, and age. Mortality was markedly elevated (20.5%) within the first 7 days of a recurrent MI. Patients who survived 7 days after a recurrent MI continued to be at increased risk of death compared with patients without a recurrent MI and the risk of death remained elevated more than two-fold a year after the recurrent MI (adjusted hazards ratio 2.4, 95% confidence interval 1.7-3.2). One-year mortality for the entire VALIANT cohort was 10.3%, whereas 38.3% of the patients were dead 1 year after recurrent MI. Early reinfarctions (within 1 month) was associated with significantly higher 30-day mortality than later reinfarctions. CONCLUSION: Even in the context of contemporary treatment, a recurrent MI confers a significantly increased risk of death in patients following a high-risk first MI. Strategies aimed at reducing recurrent MI will thus likely prolong survival in post-MI survivors.
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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) | 0.000 | 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) | 0.001 | 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".