Single Anti-Platelet Therapy versus Dual Anti-Platelet Therapy after Transcatheter Aortic Valve Replacement: A Meta-Analysis
Bibliographic record
Abstract
Background: The optimal anti-platelet regimen after transcatheter aortic valve replacement (TAVR) remains uncertain. The objective of this study was to compare the efficacy and safety of single anti-platelet therapy (SAPT) vs. dual anti-platelet therapy (DAPT) after transcatheter aortic valve replacement (TAVR). Methods: Electronic databases were searched for randomized and observational studies, which compared SAPT versus DAPT after TAVR. The primary outcomes were all-cause mortality, and major bleeding. Summary adjusted risk ratios (RR) were calculated using a Der-Simonian and Liard model. The risk of bias of the included studies was assessed by the Cochrane scale and New-castle Ottawa assessment tool. Results: A total of 10 studies with 2,412 patients were included. There was no difference in 30-days all-cause mortality (RR 1.19, 95% CI 0.79–1.81, p = 0.41, I 2 = 0.0%) and at the longest available follow up (i.e. mean 6.4 months) (RR 1.03, 95% CI 0.69–1.57, p = 0.86, I 2 = 0.0%). The risk of major bleeding was higher in the DAPT group (RR 2.14, 95% CI 1.37–3.31, p = 0.001). These findings were consistent on analyzing randomized versus observational studies (Pinteraction = 0.97, and 0.76 for all-cause mortality and major bleeding, respectively). There was no difference in the risk of life-threatening bleeding, major vascular complications, myocardial infarction, and stroke between both groups (all p-values > 0.05). Conclusion: DAPT post TAVR is associated with an increased risk of major bleeding with no benefit on mortality, stroke, or myocardial infarction. The evidence is driven mainly from observational studies, and therefore future high quality randomized trials are needed.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.056 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| 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".