H2 Receptor Antagonists versus Proton Pump Inhibitors in Patients on Dual Antiplatelet Therapy for Coronary Artery Disease: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Mitigating the gastrointestinal (GI) bleeding risks of dual antiplatelet therapy (DAPT) is a common clinical concern. While proton pump inhibitors (PPIs) remain the most effective therapy, their adverse events warrant considering alternatives, including Histamine 2 receptor antagonists (H2RAs). METHODS: We searched for randomized controlled trials in MEDLINE, EMBASE, PubMed, and Cochrane Central Register of Controlled Trials, published from 1980 to 2016. After screening, 10 trials were eligible. We compared PPIs to H2RAs in patients on DAPT in terms of 2 clinical and one laboratory outcomes; GI complications, major adverse cardiovascular events (MACE) and high on-treatment platelet reactivity (HTPR). Clinical and statistical inter-study heterogeneity was low for all 3 outcomes (I2 = 0%, p > 0.05 for all). RESULTS: Fixed effects meta-analysis suggested that PPIs were superior to H2RAs in preventing GI complications (OR 0.28, 95% CI 0.17-0.48) but with higher risk of HTPR (OR 1.28, 95% CI 1.030-1.60) though without a higher incidence of MACE (OR 0.99, 95% CI 0.55-1.77). CONCLUSIONS: PPIs are superior to H2RAs for gastroprotection in patients on DAPT. However, PPIs are associated with HTPR, with no significant difference demonstrated in MACE. Based on currently available data, the use of PPIs may be warranted in selected patients on DAPT deemed at risk for GI complications.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".