Transcatheter aortic valve implantation: latest evidence,gaps in knowledge, and future directions
Bibliographic record
Abstract
Aortic stenosis (AS) remains the most prevalent valvular heart disease worldwide and is increasingly managed through transcatheter aortic valve implantation (TAVI). With the 2025 ESC/EACTS Guidelines lowering the age threshold for TAVI to 70 years, the focus has shifted from short-term survival to lifetime management, necessitating rigorous evaluation of device durability, coronary access, and biological valve degeneration. This review synthesizes the latest evidence-based strategies for TAVI, contrasting randomized trial data with long-term registry findings. We critically analyze the hemodynamic trade-offs between self-expandable valves and balloon-expandable valves, particularly in patients with small aortic annuli, where SEVs demonstrate superior indexed effective orifice areas and reduced rates of patient-prosthesis mismatch (SMALL-TAVI registry). We further examine the expansion of indications into complex anatomical subsets, including bicuspid aortic valves (BIVOLUTX, STABILITY) and pure aortic regurgitation, where dedicated anchoring mechanisms are required to mitigate the risk of valve migration. Procedural optimization is addressed through the "minimalist" TAVI pathway (BENCHMARK registry), which emphasizes conscious sedation and ultrasound-guided vascular access to reduce length of stay without compromising safety. Finally, we discuss emerging biomarkers (MMP-3, osteopontin) and the role of epicardial adipose tissue as novel predictors of structural valve deterioration, signaling a potential shift toward biological modulation of valvular disease.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".