Acute Coronary Findings at Autopsy in Heart Failure Patients With Sudden Death
Bibliographic record
Abstract
BACKGROUND: Sudden unexpected death frequently occurs in chronic heart failure. The importance of acute coronary events in triggering sudden death (SD) is unclear. METHODS AND RESULTS: We evaluated at autopsy the prevalence of acute coronary findings (coronary thrombus, ruptured plaque, or myocardial infarction [MI]) and their relation to SD. Autopsy results in 171 patients in the randomized ATLAS trial were reviewed. The prevalence of acute coronary findings was 33%: in 54% of patients with significant coronary artery disease (CAD) who died suddenly, 32% who died of myocardial failure, but in non-CAD patients, they were present in only 5% and 10% respectively. The percentage of patients classified as dying of MI was 28% in the autopsy group versus 4% in the nonautopsied group (P<0.0001). Of the autopsied group with acute MI, 97% (31 of 32 patients) with SD and 40% (6 of 15 patients) with myocardial failure did not have the MI diagnosed during life. When undiagnosed MI was classified as "sudden unexpected" or "myocardial failure" from clinical information only, the distribution of death causes was similar in the autopsy and nonautopsied groups. CONCLUSIONS: Acute coronary findings are frequent and usually not clinically diagnosed in heart failure patients with CAD, particularly in those dying suddenly, suggesting the importance of acute coronary events as a trigger for SD in this setting.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".