Effects of Ticagrelor and Clopidogrel on Coronary Microcirculation in Patients with Acute Myocardial Infarction
Bibliographic record
Abstract
INTRODUCTION: Clopidogrel has been demonstrated to be effective in improving coronary microcirculation (CM) among patients with ST-elevation myocardial infarction (STEMI) treated with fibrinolytics. Ticagrelor is a more potent adenosine diphosphate (ADP) receptor blocker proven to be superior to clopidogrel among patients with acute coronary syndromes. The present study aimed to compare the effects of ticagrelor and clopidogrel on CM in patients with STEMI treated with fibrinolytics. METHODS: The present study prospectively included 48 patients participating in the TREAT trial, which randomly assigned patients with STEMI undergoing fibrinolysis to ticagrelor versus clopidogrel. The primary endpoint of this study was the evaluation of the CM using the global myocardial perfusion score index (global MPSI) obtained by myocardial contrast echocardiography (MCE). Platelet aggregation to ADP was evaluated by Multiplate® and expressed as area under the curve (AUC). RESULTS: The global MPSI demonstrated no differences between the groups [mean 1.4 (1.2-1.5) in the ticagrelor group and 1.2 (1.2-1.5) in the clopidogrel group (p = 0.41)]. Platelet aggregability was lower in the ticagrelor group (18.1 ± 9.7 AUC), compared to the clopidogrel group (26.1 ± 12.5 AUC, p = 0.01). CONCLUSION: We found no improvement in coronary microcirculation with ticagrelor compared to clopidogrel among patients with STEMI treated with fibrinolytics, despite the fact that platelet aggregation to ADP was lower with ticagrelor. CLINICAL TRIALS REGISTRATION: NCT03104062.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".