Adverse Impact of Bleeding on Prognosis in Patients With Acute Coronary Syndromes
Bibliographic record
Abstract
BACKGROUND: The use of multiple antithrombotic drugs and aggressive invasive strategies has increased the risk of major bleeding in acute coronary syndrome (ACS) patients. It is not known to what extent bleeding determines clinical outcome. METHODS AND RESULTS: Using Cox proportional-hazards modeling, we examined the association between bleeding and death or ischemic events in 34,146 patients with ACS enrolled in the Organization to Assess Ischemic Syndromes and the Clopidogrel in Unstable Angina to Prevent Recurrent Events studies. Patients with major bleeding were older, more often had diabetes or a history of stroke, had a lower blood pressure and higher serum creatinine, more often had ST-segment changes on the presenting ECG, and had a 5-fold-higher incidence of death during the first 30 days (12.8% versus 2.5%; P < 0.0001) and a 1.5-fold-higher incidence of death between 30 days and 6 months (4.6% versus 2.9%; P = 0.002). Major bleeding was independently associated with an increased hazard of death during the first 30 days (hazard ratio, 5.37; 95% CI, 3.97 to 7.26; P < 0.0001), but the hazard was much weaker after 30 days (hazard ratio, 1.54; 95% CI, 1.01 to 2.36; P = 0.047). The association was consistent across subgroups according to cointerventions during hospitalization, and there was an increasing risk of death with increasing severity of bleeding (minor less than major less than life-threatening; P for trend = 0.0009). A similar association was evident between major bleeding and ischemic events, including myocardial infarction and stroke. CONCLUSIONS: In ACS patients without persistent ST-segment elevation, there is a strong, consistent, temporal, and dose-related association between bleeding and death. These data should lead to greater awareness of the prognostic importance of bleeding in ACS and should prompt evaluation of strategies to reduce bleeding and thereby improve clinical 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".