Transcatheter Aortic Valve Replacement in Bicuspid Aortic Valve Stenosis
Bibliographic record
Abstract
After 15 years of successive randomized, controlled trials, indications for transcatheter aortic valve replacement (TAVR) are rapidly expanding. In the coming years, this procedure could become the first line treatment for patients with a symptomatic severe aortic stenosis and a tricuspid aortic valve anatomy. However, randomized, controlled trials have excluded bicuspid aortic valve (BAV), which is the most frequent congenital heart disease occurring in 1% to 2% of the total population and representing at least 25% of patients 80 years of age or older referred for aortic valve replacement. The use of a less invasive transcatheter therapy in this elderly population became rapidly attractive, and approximately 10% of patients currently undergoing TAVR have a BAV. The U.S. Food and Drug Administration and the "European Conformity" have approved TAVR for low-risk patients regardless of the aortic valve anatomy whereas international guidelines recommend surgical replacement in BAV populations. Given this progressive expansion of TAVR toward younger and lower-risk patients, heart teams are encountering BAV patients more frequently, while the ability of this therapy to treat such a challenging anatomy remains uncertain. This review will address the singularity of BAV anatomy and associated technical challenges for the TAVR procedure. We will examine and summarize available clinical evidence and highlight critical knowledge gaps regarding TAVR utilization in BAV patients. We will provide a comprehensive overview of the role of computed tomography scans in the diagnosis, and classification of BAV and TAVR procedure planning. Overall, we will offer an integrated framework for understanding the current role of TAVR in the treatment of bicuspid aortic stenosis and for guiding physicians in clinical decision-making.
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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 | 0.002 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".