Redo Transcatheter Aortic Valve Implantation in the Lotus Mechanically Expanded Transcatheter Heart Valve: Bench-Top Analysis, Clinical Experience, and Procedural Guidance
Bibliographic record
Abstract
BACKGROUND: Redo transcatheter aortic valve implantation (TAVI) is increasing as patients outlive their transcatheter heart valves (THVs) and present with bioprosthetic valve failure. The Lotus mechanically expanded THV has unique design characteristics, which have specific implications for Redo TAVI. METHODS: The design features of the Lotus valve and their relevance to Redo TAVI were reviewed. Bench-top analysis of Redo TAVI was performed using different contemporary THVs. Procedural and outcome data were obtained from 10 patients who had undergone Redo TAVI for Lotus bioprosthetic valve failure in 5 centers. Recommendations for performing Redo TAVI in Lotus are made, based on these findings. RESULTS: The Lotus leaflets extend from the frame inflow, with a Neoskirt of only 13 mm, hence a low risk of coronary obstruction during Redo TAVI. The Lotus frame posts prevent full apposition of the Redo prosthesis in the upper part of the frame, while implantation of the Redo THV above the Lotus inflow leads to inadequate apposition of the Lotus leaflets. Inflow-to-inflow positioning is therefore recommended for effective sealing and leaflet pinning. The Lotus locking mechanism prevents overexpansion of the frame, limiting Redo THV oversizing. Redo TAVI was favorable with SAPIEN 3, Evolut, and Navitor THVs on bench-top analysis but not with ACURATE Neo 2 due to the upper crowns and short stent preventing inflow-to-inflow deployment. Case review demonstrated satisfactory outcomes in 10 patients treated with Evolut (n=6), SAPIEN 3 (n=3), and Portico (n=1) valves, with no mortality, major morbidity, or coronary obstruction. Three patients had residual mean gradient ≥20 mm Hg, including 2 of 3 SAPIEN cases. Guidance on procedural planning, valve choice, sizing, and positioning is provided. CONCLUSIONS: Redo TAVI in Lotus requires an understanding of unique design characteristics, and adherence to key procedural recommendations, but can be safely and effectively performed with most contemporary valve types.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".