Mono versus Dual Antiplatelet Therapy after Transcatheter Aortic Valve Replacement: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background Dual antiplatelet therapy (DAPT) is routinely prescribed after transcatheter aortic valve replacement (TAVR) despite the lack of definitive data demonstrating its superiority over mono-antiplatelet therapy (MAPT). We aim to investigate the benefits of DAPT versus MAPT and at different follow-up time points post TAVR. Methods A systematic search was conducted for studies investigating DAPT versus MAPT in patients who underwent TAVR. The primary outcome was net adverse clinical events (NACE) at longest reported follow-up, defined as a composite end-point of all-cause mortality, major stroke, myocardial infarction (MI), and combined life threatening and major bleeding. Secondary endpoints included each outcome individually. We performed subgroup analysis according to study type (randomized control trials vs. observational studies) and follow-up duration post-TAVR (≤ 30 days, between 3 and 6 months, and ≥ 1 year). Results Twelve studies with 9,650 patients were included. Post-TAVR MAPT was associated with significantly reduced NACE (0.60 [0.45, 0.81], p < 0.001), all-cause mortality (OR 0.54 [0.33, 0.88], p = 0.01), and combined life threatening and major bleeding (0.57 [0.39, 0.84], p = 0.005) in the first 30 days after the procedure when compared to DAPT. The difference in outcomes diminishes with longer-term follow up durations (3–6 month or ≥ 6-month). No differences were seen with other secondary endpoints. Conclusion MAPT is associated with improved outcomes compared to DAPT in the first 30 days post-TAVR with no difference in outcomes on longer-term follow up. Future prospective, adequately powered, multicenter, placebo-controlled, randomized double-blinded cohort studies are warranted to confirm our findings.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".