Impact of concomitant use of proton pump inhibitors and clopidogrel or ticagrelor on clinical outcomes in patients with acute coronary syndrome.
Bibliographic record
Abstract
BACKGROUND: There is great debate on the possible adverse interaction between proton pump inhibitors (PPIs) and clopidogrel. In addition, whether the use of PPIs affects the clinical efficacy of ticagrelor remains less known. We aimed to determine the impact of concomitant administration of PPIs and clopidogrel or ticagrelor on clinical outcomes in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI). METHODS: We retrospectively analyzed data from a "real world", international, multi-center registry between 2003 and 2014 (n = 15,401) and assessed the impact of concomitant administration of PPIs and clopidogrel or ticagrelor on 1-year composite primary endpoint (all-cause death, re-infarction, or severe bleeding) in patients with ACS after PCI. RESULTS: Of 9429 patients in the final cohort, 54.8% (n = 5165) was prescribed a PPI at discharge. Patients receiving a PPI were older, more often female, and were more likely to have comorbidities. No association was observed between PPI use and the primary endpoint for patients receiving clopidogrel (adjusted HR: 1.036; 95% CI: 0.903-1.189) or ticagrelor (adjusted HR: 2.320; 95% CI: 0.875-6.151) (P interaction = 0.2004). Similarly, use of a PPI was not associated with increased risk of all-cause death, re-infarction, or a decreased risk of severe bleeding for patients treated with either clopidogrel or ticagrelor. CONCLUSIONS: In patients with ACS following PCI, concomitant use of PPIs was not associated with increased risk of adverse outcomes in patients receiving either clopidogrel or ticagrelor. Our findings indicate it is reasonable to use a PPI in combination with clopidogrel or ticagrelor, especially in patients with a higher risk of gastrointestinal bleeding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".