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 We included 11 RCTs (n = 6415), including 1 unpublished RCT.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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.129 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".