Comparison of Prasugrel and Ticagrelor for Patients with Acute Coronary Syndrome: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
INTRODUCTION: There have been inconsistent data on the direct comparison of prasugrel and ticagrelor. This meta-analysis was conducted to summarize the current available evidence. METHODS: We performed a meta-analysis (PROSPERO-registered CRD42020166810) of randomized trials up to February 2020 that compared prasugrel and ticagrelor in acute coronary syndrome with respect to the composite endpoint of myocardial infarction (MI), stroke, or cardiovascular death and secondary endpoints including MI, stroke, cardiovascular death, major bleeding (Bleeding Academic Research Consortium (BARC) type 2 or above), stent thrombosis, all-cause death, and other safety outcomes. RESULTS: Of the 11 eligible RCTs with 6,098 patients randomized to prasugrel (n = 3,050) or ticagrelor (n = 3,048), 180 and 207 had the composite endpoint events in the prasugrel arm and the ticagrelor arm, respectively, over a weighted mean follow-up period of 11 ± 2 months. Compared with prasugrel, the ticagrelor group had similar risk in the primary composite endpoint (risk ratio [RR] = 1.17; 95% CI = 0.96-1.42; p = 0.12, I2 = 0%). Compared to prasugrel, there was no significant difference associated with the ticagrelor groups with respect to stroke (RR = 1.05; 95% CI = 0.66-1.67; p = 0.84, I2 = 0%), cardiovascular death (RR = 1.01; 95% CI = 0.75-1.36; p = 0.95, I2 = 0%), BARC type 2 or above bleeding (RR = 1.16; 95% CI = 0.89-1.52; p = 0.26, I2 = 0%), stent thrombosis (RR = 1.58; 95% CI = 0.90-2.76; p = 0.11, I2 = 0%), and all-cause death (RR = 1.10; 95% CI = 0.86-1.43; p = 0.45, I2 = 0%) except MI (RR = 1.38; 95% CI = 1.05-1.81; p = 0.02, I2 = 0%) Conclusion: Compared with prasugrel, ticagrelor did not reduce the primary composite endpoint of MI, stroke, and cardiovascular death at a weighted mean follow-up of 11 months. There was no significant difference between the secondary outcomes except MI.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".