In‐hospital outcomes of STEMI patients on warfarin undergoing primary PCI
Bibliographic record
Abstract
OBJECTIVES: We sought to describe the safety and efficacy outcomes of patients on warfarin presenting with ST-elevation myocardial infarction (STEMI). BACKGROUND: Limited data exist on the outcomes and optimal management of STEMI patients on warfarin undergoing primary percutaneous coronary intervention (PCI). METHODS: Baseline characteristics and outcomes were prospectively collected for 2,390 consecutive STEMI patients referred for primary PCI. Patients were stratified based on warfarin use at baseline. The primary safety endpoint was the rate of in-hospital bleeding (a composite of major bleeding or minor bleeding) according to the thrombolysis in myocardial infarction (TIMI) classification. Efficacy endpoints included major adverse cardiovascular events (MACE), defined as death, myocardial infarction, or stroke, as well as intracranial bleeding, cardiogenic shock, and length of stay. Multiple logistic regression was used to determine if warfarin was independently associated with bleeding and MACE. RESULTS: Warfarin patients (n = 59 vs. n = 2,331) were significantly older (73.2 years vs. 61.7 years; P < 0.01), and more likely to present as Killip Class IV (13.6% vs. 2.7%; P < 0.01). TIMI major/minor bleeding occurred in 30.4% of the warfarin patients and 14.2% of the control patients (P < 0.01). After adjustment warfarin was independently associated with an increased risk of bleeding (OR 2.08; P = 0.04). Warfarin patients also had an increased frequency of MACE (20.3% vs. 5.9%; P < 0.01), though this was not significant after adjustment (OR 2.00; P = 0.10). CONCLUSIONS: STEMI patients on warfarin referred for primary PCI are more likely to experience bleeding. New strategies are needed to optimize the management and minimize bleeding in this high-risk population.
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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) | 0.000 | 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 | 0.000 | 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".