Abstract 16894: The Effect of Cangrelor on Cardiovascular and Bleeding Events in Patients With Peripheral Artery Disease - Insights From CHAMPION PHOENIX
Bibliographic record
Abstract
Background: Patients with peripheral artery disease (PAD) are high risk for adverse cardiovascular and bleeding events. In CHAMPION-PHOENIX, cangrelor, an intravenous P2Y12 inhibitor, reduced rates of ischemic events in patients undergoing PCI. Hypothesis: We hypothesize that cangrelor will safely reduce ischemic events in patients with PAD undergoing PCI. Methods: A total of 11,145 patients were randomly assigned in a double-dummy, double-blind manner to either cangrelor followed by clopidogrel 600 mg or to clopidogrel loading at PCI. The primary endpoint was a composite of death, MI, ischemia-driven revascularization (IDR), or stent thrombosis (ST) at 48 hours. Results: 837 (8%) patients with PAD and 9,994 (90%) patients with no prior history of PAD underwent PCI. Among the PAD cohort the primary endpoint occurred in 20 (4.5%) cangrelor vs. 44 (11.4%) clopidogrel patients (OR [95% CI] = 0.36 [0.21, 0.63]), and 235 (4.7%) cangrelor vs. 276 (5.5%) clopidogrel patients (OR [95%CI] = 0.86 [0.72, 1.03]) without PAD (p for interaction = 0.003). Among the PAD cohort the rate of GUSTO severe/life-threatening bleeding was 0.4% cangrelor vs. 0% clopidogrel (p = 0.19), and 0.1% cangrelor vs. 0.1% clopidogrel patients (OR [95%CI] = 1.78 [0.52, 6.07], p = 0.35) without PAD (p for interaction = 0.34). The rate of blood transfusion in the PAD cohort was 0.9% cangrelor vs. 0% clopidogrel (p = 0.06), and 0.4% cangrelor vs. 0.3% clopidogrel patients (OR [95%CI] = 1.42 [0.73, 2.76], p = 0.30) without PAD (p for interaction = 0.13). Conclusion: In CHAMPION-PHOENIX, cangrelor significantly reduced ischemic events with no significant increase in severe bleeding or transfusions in patients with PAD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".