Antithrombotic therapy in patients after transcatheter aortic valve implantation: a network meta-analysis
Bibliographic record
Abstract
AIMS: The optimal antithrombotic therapy to balance the risk of thrombosis and bleeding in patients who undergo transcatheter aortic valve implantation (TAVI) is unknown. This systematic review/network meta-analysis of randomized controlled trials (RCTs) aimed to evaluate the efficacy and safety of different oral anticoagulant (OAC) and antiplatelet regimens in patients post-TAVI. METHODS AND RESULTS: MEDLINE, Embase, CENTRAL, and ClinicalTrials.gov were searched from inception to April 2023. Co-primary outcomes were all-cause death and major bleeding. We conducted Bayesian network meta-analyses to compare all interventions simultaneously. For each outcome, we generated odds ratios (ORs) with 95% credible intervals using a random-effects model with informative priors, and ranked interventions based on mean surface under the cumulative ranking curve. We included 11 RCTs (n = 6415), including 1 unpublished RCT. Three trials enrolled patients with an indication for an OAC. Overall risk of bias was low or with some concerns. Median age was 81 years. Median follow-up was 6 months. The combination of OAC plus single antiplatelet therapy (SAPT) increased the risk of all-cause death compared with dual antiplatelet therapy (DAPT) (OR 1.78, 95% credible interval 1.15-2.77). No other comparisons for all-cause death were significantly different. For major bleeding, SAPT reduced the risk compared with DAPT, direct-acting OAC, and OAC + SAPT (OR 0.20-0.40), and DAPT reduced the risk compared with OAC + SAPT. SAPT and DAPT ranked best for all-cause death, while SAPT ranked best for major bleeding. CONCLUSION: In post-TAVI patients, SAPT may provide the optimal balance of reducing thrombotic events while minimizing the risk of 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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.045 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".