Red blood cell transfusion and mortality after transcatheter aortic valve implantation via transapical approach: A propensity-matched comparison from the TRITAVI registry
Bibliographic record
Abstract
Objective: Bleeding is frequent during transcatheter aortic valve implantation (TAVI), especially when performed through a transapical approach (TA), and is associated with a worse prognosis. The present study aims to test the implication of red blood cell (RBC) transfusion and the optimal transfusion strategy in this context. Methods: Among 11,265 participants in the multicenter TRITAVI (Transfusion Requirements in Transcatheter Aortic Valve Implantation) registry, 548 patients (4.9%) who received TA-TAVI at 19 European centers were included. One-to-one propensity score matching was performed to reduce treatment selection bias and potential confounding among transfused versus non-transfused patients. The primary endpoint of the study was the 30-day occurrence of all-cause mortality. Results: 209 patients (38 %) received RBC transfusions. The primary endpoint occurred in 47 (8.6 %) patients. Propensity score matching identified 188 pairs of patients with and without RBC transfusion. In the propensity score-matched analysis, RBC transfusion was associated with increased 30-day mortality (HR 3.35, 95 % CI 1.51 - 7.39; p = 0.002). At multivariable cox regression analysis, RBC transfusion was an independent predictor of 30-day mortality (HR 3.07, 95 % CI 1.01-9.41, p = 0.048), as well as baseline ejection fraction (HR 0.96, 95 % CI 0.92-0.99, p = 0.043), and acute kidney injury (HR 3.95, 95 % CI 1.11-14.05, p = 0.034). Conclusions: RBC transfusion is an independent predictor of short-term mortality in patients undergoing TA-TAVI, regardless of major bleeding.Clinical trial registration: https://www.clinicaltrials.gov Unique identifier: NCT03740425.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".