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 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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".