Inflow-to-Outflow Stent Frame Expansion, Ellipticity, and Decoupling in Evolut TAVR: Implications for Mid-term Hemodynamic Performance
Bibliographic record
Abstract
Background The native aortic annulus for self-expanding transcatheter aortic valve replacement (TAVR) has variable ellipticity. A noncircular and underexpanded transcatheter aortic valve (TAV) may impact hemodynamic performance. This study aimed to quantify Evolut TAV (Medtronic) frame ellipticity and expansion 30 days post-TAVR and evaluate their impact on 1-year hypoattenuating leaflet thickening and 4-year hemodynamics. Methods We retrospectively evaluated 184 patients from the Evolut Low Risk substudy with high-quality computed tomography images. Frame ellipticity ratio and percent expansion were quantified at each frame node level 30 days after TAVR. Variables associated with frame deformation, 1-year hypoattenuating leaflet thickening, and 4-year hemodynamics were identified. Results Mean Evolut frame ellipticity was highest at the inflow (1.18 ± 0.08) and lowest at the functional leaflet region (1.05 ± 0.03) and frame outflow (1.04 ± 0.03). Frame expansion was lowest at the inflow (83.8% ± 4.9%) and highest at the functional leaflet region (97.8% ± 1.7%). TAV frame circularity and expansion significantly increased from the annular level to the leaflet region ( P < .001). Mean gradient, effective orifice area, and paravalvular regurgitation at 4 years were not affected by Evolut TAV's relative noncircularity and underexpansion at the frame inflow. Frame underexpansion at the leaflet region, however, was associated with a smaller effective orifice area at 4 years. Conclusions Evolut frame deformation at the inflow did not affect the circularity and expansion of the stent at the functional leaflet region. Mid-term (4-year) Evolut hemodynamic performance does not appear to be impacted by frame inflow geometry.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".