Non-fatal cardiovascular events preceding sudden cardiac death in patients with an acute myocardial infarction complicated by heart failure: insights from the high-risk myocardial infarction database
Bibliographic record
Abstract
AIMS: Among patients with acute myocardial infarction (AMI) complicated by heart failure [HF; clinical HF or left ventricular (LV) systolic dysfunction], we explored the probability of subsequent non-fatal cardiovascular (CV) events and sudden cardiac death (SCD). METHODS AND RESULTS: The high-risk myocardial infarction (HRMI) database contains 28 771 patients with signs of HF or reduced LV ejection fraction (<40%) after AMI. We evaluated the temporal association between SCD with preceding non-fatal CV event [HF hospitalization, recurrent myocardial infarction (MI), or stroke]. Median follow-up was 1.9 years. Mean age was 65.0 ± 11.5 years and 70% were male. The incidence of CV death was 7.9 per 100 patient-years and for SCD was 3.1 per patient-years (40% of CV deaths). The incidence of SCD preceded by HF hospitalization was greater than SCD without preceding HF hospitalization (P < 0.05). However, overall, SCD was less likely to be preceded by a non-fatal CV event compared to other causes of death: 9.6% of SCD events were preceded by an MI (vs. 46.6% for non-sudden CV death); 17.0% of SCD events were preceded with an HF hospitalization (vs. 25.4% for non-sudden CV death); and 2.7% of SCD events were preceded by stroke (vs.12.9% for non-sudden CV death). CONCLUSION: Among patients with AMI complicated by HF, SCD, compared with other causes of death, was less likely to be preceded by a non-fatal CV event. As patients are less likely to have preceding non-fatal CV events to alert the healthcare team of a possible impending SCD event, additional strategies for risk stratification for SCD are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".