A bench test study of bioprosthetic valve fracture performed before versus after transcatheter valve-in-valve intervention
Bibliographic record
Abstract
AIMS: Bioprosthetic valve fracture (BVF) may improve transvalvular gradients and transcatheter heart valve (THV) expansion during VIV interventions. However, the optimal timing of BVF is unknown. We assessed the impact of timing of BVF (before versus after) for valve-in-valve (VIV) intervention, on hydrodynamic function and THV expansion. METHODS AND RESULTS: Three THV designs were assessed, a 23 mm SAPIEN 3 (S3), small ACURATE neo (ACn) and 23 mm Evolut R, deployed into 21 mm Mitroflow bioprosthetic surgical valves. We evaluated each THV in three groups: 1) no BVF, 2) BVF before VIV, and 3) BVF after VIV. Hydrodynamic testing was performed using a pulse duplicator to ISO 5840:2013 standard. Transvalvular gradients were lower when BVF was performed after VIV for the S3 (no BVF 15.5 mmHg, BVF before VIV 8.0 mmHg, BVF after VIV 5.6 mmHg), and the ACn (no BVF 9.8 mmHg, BVF before VIV 8.4 mmHg, BVF after VIV 5.1 mmHg). Transvalvular gradients were similar for the Evolut R, irrespective of performance of BVF or timing of BVF. BVF performed after VIV resulted in better expansion in all three THV designs. The ACn and Evolut R samples all had a mild degree of pinwheeling, and BVF timing did not impact on pinwheeling severity. The S3 samples had severe pinwheeling with no BVF, and significant improvement in pinwheeling when BVF was performed after VIV. CONCLUSIONS: BVF performed after VIV was associated with superior THV expansion in all three THV designs tested, with lower residual transvalvular gradients in the S3 and ACn THVs. The Evolut R had similar hydrodynamic performance irrespective of BVF timing. Timing of BVF has potential implications on THV function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".