Switching to Clopidogrel in Patients With Acute Coronary Syndrome Managed With Percutaneous Coronary Intervention Initially Treated With Prasugrel or Ticagrelor: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Objective: To evaluate the effects of switching from ticagrelor or prasugrel to clopidogrel in acute coronary syndrome (ACS) patients managed with percutaneous coronary intervention on major adverse cardiovascular events (MACEs) and bleeding. Data Sources: We searched MEDLINE, EMBASE, CENTRAL, bibliographies of relevant articles, and clinicaltrials.gov for eligible articles published from inception to January 27, 2019. Study Selection and Data Extraction: We included randomized controlled trials (RCTs) and cohort and case-control studies that reported on ≥1 outcome of interest. Primary outcomes were MACE and major bleeding, and the secondary outcome was any bleeding. Data Synthesis: From 483 articles, we included 7 relevant studies (2 RCTs, 5 cohort studies) at high/unclear risk of bias. Random-effects meta-analysis revealed inconclusive effects on MACE (hazard ratio [HR] = 1.00, 95% CI = 0.59-1.68; I 2 = 82%), major bleeding (HR = 0.51; 0.19-1.35; I 2 = 91%), and any bleeding (HR = 0.64; 0.38-1.07; I 2 = 85%). Similar nonsignificant results were obtained in secondary analyses evaluating risk ratios. Relevance to Patient Care and Clinical Practice: Ticagrelor and prasugrel, are now considered preferred therapy over clopidogrel in patients with ACS. Switching from these potent P2Y 12 inhibitors to clopidogrel is commonly performed to reduce bleeding risk, other adverse effects, or costs. Current best-available evidence is inconclusive regarding the effects of switching to clopidogrel on the risk of MACE and bleeding. Overall, studies were underpowered to detect clinically important differences. Conclusions: Until adequately powered trials demonstrate an advantage to switching to clopidogrel from prasugrel or ticagrelor, clinicians may consider this approach as clinically indicated on an individual, case-by-case basis.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.005 | 0.006 |
| 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) | 0.003 | 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".