Continued non-vitamin K antagonist oral anticoagulants versus vitamin K antagonists during transcatheter aortic valve implantation
Bibliographic record
Abstract
BACKGROUND: One-third of patients undergoing transcatheter aortic valve implantation (TAVI) have an indication for long-term oral anticoagulation (OAC). AIMS: We aimed to investigate whether continued non-vitamin K antagonist oral anticoagulant (NOAC) therapy compared with continued vitamin K antagonist (VKA) therapy during TAVI is equally safe and effective. Methods: Consecutive patients on OAC with either NOAC or VKA undergoing transfemoral TAVI at five European centres were enrolled. The primary outcome measure was a composite of major/life-threatening bleeding, stroke, and all-cause mortality at 30 days. RESULTS: In total, 584 patients underwent TAVI under continued OAC with 294 (50.3%) patients receiving VKA and 290 (49.7%) patients receiving NOAC. At 30 days, the composite primary outcome had occurred in 51 (17.3%) versus 36 (12.4%) patients with continued VKA and with continued NOAC, respectively (odds ratio [OR] 0.68, 95% confidence interval [CI]: 0.43-1.07; p=0.092). Rates of major/life-threatening bleeding (OR 0.87, 95% CI: 0.52-1.47; p=0.606) and stroke (OR 1.02, 95% CI: 0.29-3.59; p=0.974) were not different between groups. In a multivariate Cox regression analysis, continued NOAC, compared with continued VKA, was associated with a lower risk for all-cause 1-year mortality (hazard ratio [HR] 0.61, 95% CI: 0.37-0.98; p=0.043). The analysis of the propensity score-matched cohort revealed similar results. CONCLUSIONS: Continued NOAC compared with continued VKA during TAVI led to comparable outcomes with regard to the composite outcome measure indicating that continued OAC with both drugs is feasible. These hypothesis-generating results need to be confirmed by a dedicated randomised controlled trial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".