Timing of bioprosthetic valve fracture in transcatheter valve-in-valve intervention: impact on valve durability and leaflet integrity
Bibliographic record
Abstract
BACKGROUND: Bioprosthetic valve fracture (BVF) can be used to improve transcatheter heart valve (THV) haemodynamics following a valve-in-valve (ViV) intervention. However, whether BVF should be performed before or after THV deployment and the implications on durability are unknown. Aims: We sought to assess the impact of BVF timing on long-term THV durability. METHODS: The impact of BVF timing was assessed using small ACURATE neo (ACn) or 23 mm SAPIEN 3 (S3) THV deployed in 21 mm Mitroflow valves compared to no-BVF controls. Valves underwent accelerated wear testing up to 200 million (M) cycles (equivalent to 5 years). At 200M cycles, THV were evaluated by hydrodynamic testing, second-harmonic generation (SHG) microscopy, scanning electron microscopy (SEM) and histology. RESULTS: , respectively. Further, SHG and SEM revealed a higher degree of superficial leaflet damage when BVF was performed after ViV for the ACn and S3. However, the histological analysis revealed significantly less damage, as determined by matrix density analysis, through the entire leaflet thickness when BVF was performed after ViV with the S3 and a similar but non-significant trend with the ACn. Conclusions: BVF performed after ViV appears to offer superior long-term EOA without increased RF. Ultrastructure leaflet analysis reveals that the timing of BVF can differentially impact leaflets, with more superficial damage but greater preservation of overall leaflet structure when BVF is performed after ViV.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".