A Comparative Pharmacodynamic Study of Ticagrelor versus Clopidogrel and Ticagrelor in Patients Undergoing Primary Percutaneous Coronary Intervention: The CAPITAL RELOAD Study
Bibliographic record
Abstract
BACKGROUND: In patients undergoing primary percutaneous coronary intervention (PPCI) ticagrelor is superior to clopidogrel in reducing cardiovascular events. This study sought to evaluate the effect of clopidogrel pretreatment on the pharmacodynamics of ticagrelor in patients undergoing PPCI. METHODS: We measured platelet reactivity using the VerifyNow P2Y12 assay at baseline, 1, 2, 4, 6, 12, 24, and 48 hours following ticagrelor bolus in patients previously loaded with clopidogrel (C+T) and in thienopyridine-naive patients (T) referred to our centre for PPCI. RESULTS: In total, 52 consecutive eligible patients with ST-elevation myocardial infarction (STEMI) were enrolled (27 C+T and 25 T). Baseline characteristics and mean baseline platelet reactivity units (PRUs) were similar between the groups. The primary endpoint, the proportion of patients achieving a PRU<208 at 2 hours, was more frequently achieved in the C+T group compared to T treatment (76.0% vs 44.4%, p= 0.026). Notably, C+T therapy resulted in fewer patients with high platelet reactivity at 1 hour (56.0% vs. 14.8%), 4 hours (100.0% vs. 61.5%) and 6 hours (100.0% vs. 64%, p<0.01 for all comparisons). Furthermore, C+T therapy was associated with lower PRU values from 2 to 48 hours. CONCLUSIONS: In patients referred for PPCI, ticagrelor bolus following clopidogrel resulted in more rapid and profound platelet inhibition, demonstrating a positive pharmacodynamic interaction. Further study is needed to determine if this pharmacodynamic effect translates into reduced clinical events.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".