Feasibility of redo-TAVI in self-expanding Evolut valves: a CT analysis from the Evolut Low Risk Trial substudy
Bibliographic record
Abstract
BACKGROUND: Transcatheter aortic valve implantation in an existing transcatheter valve (redo-TAVI) pins the index valve leaflets in the open position (neoskirt), which can cause coronary flow compromise and limit access. Whether anatomy may preclude redo-TAVI in self-expanding Evolut valves is unknown. AIMS: We aimed to evaluate the anatomical feasibility of redo-TAVI by simulating implantation of a balloon-expandable SAPIEN 3 (S3) within an Evolut or an Evolut within an Evolut. METHODS: A total of 204 post-TAVI computed tomography (CT) scans from the Evolut Low Risk CT substudy were analysed. Five redo-TAVI positions were evaluated: S3-in-Evolut inflow-to-inflow, S3 outflow at Evolut nodes 4, 5, and 6, and Evolut-in-Evolut inflow-to-inflow. Univariable modelling identified pre-TAVI clinical characteristics, CT anatomical parameters, and procedural variables associated with coronary flow compromise using the neoskirt height and post-TAVI aortic root dimensions. RESULTS: The risk of coronary flow compromise was lowest when the S3 outflow was at Evolut node 4 (20%) and highest when at Evolut node 6 (75%). The highest likelihood of preserving coronary accessibility occurred with the S3 outflow at Evolut node 4. Female sex and higher body mass index were associated with a higher risk of coronary flow compromise, as were a smaller annulus diameter, lower sinus of Valsalva height and width, shorter coronary height, smaller sinotubular junction diameter, and shallower Evolut implant depth. CONCLUSIONS: The feasibility of redo-TAVI after Evolut failure is multifactorial and relates to the native annular anatomy, as well as the implantation depth of the index and second bioprostheses. Placement of an S3 at a lower Evolut position may reduce the risk of coronary flow compromise while preserving coronary access. CLINICALTRIALS: gov: NCT02701283.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".