Does Comorbidity Account for the Excess Mortality in Patients With Major Bleeding in Acute Myocardial Infarction?
Bibliographic record
Abstract
BACKGROUND: Analyses from randomized controlled trials suggest that bleeding in patients with acute myocardial infarction is associated with poor outcomes. Because these data are not generalizable to all patients with acute myocardial infarction, we sought to better understand the scope of this problem in a "real-world" setting. METHODS AND RESULTS: We examined the frequency of major bleeding in 40,087 patients with acute myocardial infarction enrolled in the Global Registry of Acute Coronary Events. Regression analyses were used to examine the association between patient and treatment characteristics, bleeding, and hospital and postdischarge outcomes. Major bleeding occurred in 2.8% of patients. These patients were older, more severely ill, and more likely to undergo invasive procedures. Patients with bleeding were more likely to die during hospitalization (hazard ratio, 1.9; 95% confidence interval, 1.6 to 2.2) but not after discharge (hazard ratio, 0.8; 95% confidence interval, 0.6 to 1.0) than patients who did not bleed. Continuation of antithrombotic therapies after day 1 was lower in patients who experienced early bleeding. Moreover, in patients who bled, hospital mortality was increased in those who discontinued aspirin, thienopyridines, or low-molecular-weight heparins. CONCLUSIONS: Major bleeding occurred in 1 in 35 patients with acute myocardial infarction; these patients accounted for approximately 10% of all hospital deaths. Nevertheless, risk of hospital mortality associated with bleeding was much lower than reported in randomized controlled trials. These data suggest that although bleeding may be causally related to adverse outcomes in some patients in the real-world setting, it is often merely a marker for patients at higher risk for adverse outcomes.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".