Bicuspid aortic valve disease: advancements and challenges of transcatheter aortic valve implantation
Bibliographic record
Abstract
Transcatheter aortic valve implantation (TAVI) has revolutionized the treatment of patients with severe aortic stenosis (AS). Initially developed for patients with tricuspid aortic valve (TAV) anatomy, the procedural success and expanding indications of TAVI have spurred interest in its application to more complex aortic valve anatomies, such as the bicuspid aortic valve (BAV). The growing interest in this specific sub-set of patients with AS is driven by the recent extension of TAVI indications to younger individuals, who exhibit a notably higher incidence of bicuspid anatomy compared with older populations. Bicuspid aortic valves present distinct anatomical and pathological complexities that pose significant challenges to the conventional TAVI approach. These include asymmetric calcification, aortic root dilation (also known as BAV aortopathy), and variations in cusp fusion patterns, which can affect valve deployment, transcatheter heart valve sealing, and long-term durability. Despite these challenges, advancements in imaging techniques, valve design, and procedural strategies have led to increased adoption of TAVI in BAV patients. However, surgical aortic valve replacement still retains a more prominent role in this group compared with patients with TAVs. This preference is partly due to the exclusion of BAV patients from almost all previous randomized controlled trials, which limits the available evidence supporting the use of TAVI in this unique cohort. This state-of-the-art review aims to provide a comprehensive overview of the current landscape of TAVI in BAV patients, including an analysis of anatomical considerations and procedural pitfalls, as well as outcomes' improvements with new device iterations. It will also explore clinical data, tackling the risks, benefits, and the evolving role of TAVI in this unique patient cohort.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".