Repeat transcatheter aortic valve implantation and implications for transcatheter heart valve performance: insights from bench testing
Bibliographic record
Abstract
BACKGROUND: THV implantation within failed surgical valves is well established. However, the implications of THV implantation within failed THVs are poorly understood. AIMS: This study aimed to assess the impact of different transcatheter heart valve (THV) designs and implant positioning strategies on hydrodynamic performance after redo transcatheter aortic valve implantation (TAVI). METHODS: THVs of varying design (SAPIEN 3, Evolut PRO, ACURATE neo, ALLEGRA, and Portico) and size were implanted inside SAPIEN XT and Evolut R THVs. Hydrodynamic function as per International Organization for Standardization (ISO) specifications was evaluated using a pulse duplicator, and multi-modality imaging was performed. RESULTS: The majority of tested THV-in-THV combinations resulted in stable anchoring of the new implant. However, some combinations resulted in unstable anchoring with the potential for dislodgement or embolisation. Hydrodynamic function was favourable for all tested THV designs implanted in the intra-annular SAPIEN XT THV. SAPIEN 3 implantation within an Evolut THV with supra-annular leaflets must be appropriately sized to ensure adequate anchoring. Avoidance of an intra-annular deployment mitigated leaflet overhang of the Evolut leaflets and optimised hydrodynamic performance. CONCLUSIONS: This study demonstrates that most THV designs and implantation strategies can result in favourable hydrodynamic performance following redo TAVI. Whether the leaflets of a failed THV are intra- or supra-annular may have important implications for the positioning of a redo-THV implant. Certain THV designs or implantation positions may be more desirable when performing redo TAVI.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".