Comparison of prasugrel and ticagrelor for patient with acute coronary syndrome: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Prasugrel and ticagrelor are both effective anti-platelet drugs for patients with acute coronary syndrome. However, there has been limited data on the direct comparison of prasugrel and ticagrelor until the recent ISAR-REACT 5 trial. Purpose To compare the efficacy of prasugrel and ticagrelor in patients with acute coronary syndrome with respect to the primary composite endpoint of myocardial infarction (MI), stroke or cardiac cardiovascular death, and secondary endpoints including MI, stroke, cardiovascular death, major bleeding (Bleeding Academic Research Consortium (BARC) type 2 or above), and stent thrombosis within 1 year. Methods Meta-analysis was performed on randomised controlled trials (RCT) up to December 2019 that randomised patients with acute coronary syndrome to either prasugrel or ticagrelor. RCTs were identified from Medline, Embase and ClinicalTrials.gov using Cochrane library CENTRAL by 2 independent reviewers with “prasugrel” and “ticagrelor” as search terms. Effect estimates with confidence intervals were generated using the random effects model by extracting outcome data from the RCTs to compare the primary and secondary clinical outcomes. Cochrane risk-of-bias tool for randomised trials (Ver 2.0) was used for assessment of all eligible RCTs. Results 411 reports were screened, and we identified 11 eligible RCTs with 6098 patients randomised to prasugrel (n=3050) or ticagrelor (n=3048). The included trials had a follow up period ranging from 1 day to 1 year. 330 events on the prasugrel arm and 408 events on the ticagrelor arm were recorded. There were some concerns over the integrity of allocation concealment over 7 trials otherwise risk of other bias was minimal. Patients had a mean age of 61±4 (76% male; 50% with ST elevation MI; 35% with non-ST elevation MI; 15% with unstable angina; 25% with diabetes mellitus; 64% with hypertension; 51% with hyperlipidaemia; 42% smokers). There was no significant difference in risk between the prasugrel group and the ticagrelor group on the primary composite endpoint (Figure 1) (Risk Ratio (RR)=1.17; 95% CI=0.97–1.41; p=0.10, I2=0%). There was no significant difference between the use of prasugrel and ticagrelor with respect to MI (RR=1.24; 95% CI=0.81–1.90; p=0.31); stroke (RR=1.05; 95% CI=0.66–1.67; p=0.84); cardiovascular death (RR=1.01; 95% CI=0.75–1.36; p=0.95); BARC type 2 or above bleeding (RR=1.17; 95% CI =0.90–1.54; p=0.24); stent thrombosis (RR=1.58; 95% CI =0.90–2.76; p=0.11). Conclusion Compared with ticagrelor, prasugrel did not reduce the primary composite endpoint of MI, stroke and cardiovascular death within 1 year. There was also no significant difference in the risk of MI, stroke, cardiovascular death, major bleeding and stent thrombosis respectively. Figure 1. Primary Objective Funding Acknowledgement Type of funding source: None
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.026 | 0.040 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".