Single vs Dual Antiplatelet Therapy Following Transcatheter Aortic Valve Implantation: A Systematic Review
Bibliographic record
Abstract
There is wide variability in prescribing of antiplatelet regimens following transcatheter aortic valve implantation (TAVI). The objective of this review was to evaluate published and unpublished reports regarding the efficacy and safety of dual antiplatelet therapy (DAPT) compared with a single antiplatelet agent in patients undergoing TAVI. We searched MEDLINE, CENTRAL, Embase, and unpublished sources of literature from inception to December 2014 using terms synonymous with TAVI and DAPT. We included randomized controlled trials (RCTs) and cohort or case-control studies that compared DAPT with a single antiplatelet agent post-TAVI. Four articles met the inclusion criteria (2 RCTs, 2 cohort studies), of which all were deemed to be at high risk of bias, for a total of 662 patients. Compared with a single antiplatelet agent, DAPT did not significantly reduce all-cause mortality (risk ratio: 1.22, 95% confidence interval: 0.72-2.09, I(2) = 0%). Due to selective outcome reporting and variable follow-up, other outcomes of interest could not be meta-analyzed; however, evaluation of individual studies demonstrated no significant reduction in thrombotic events with DAPT and a similar or higher risk of bleeding. Current evidence, though limited by low methodological quality, suggests a lack of benefit and potential harm with DAPT compared with a single antiplatelet agent in patients post-TAVI. Therefore, clinicians should evaluate the use of DAPT in patients post-TAVI on a case-by-case basis until more robust evidence is available to guide practice.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".