Effects of hemodynamic conditions and valve sizing on leaflet bending stress in self‐expanding transcatheter aortic valve: An in vitro study
Bibliographic record
Abstract
Transcatheter aortic valve (TAV) replacement has become a viable alternative to surgery for high and intermediate risk patients with severe aortic stenosis. This technology may extend to the younger and lower risk patients. In this population, long-term durability of the TAV is key. Increased leaflet mechanical stress is one of the main determinants of valve structural deterioration. This in vitro study aims at evaluating leaflet bending stress (LBS) in the self-expanding TAV for different valve sizes, stroke volumes (SV), and degrees of valve oversizing (OS). Three different sizes (23, 26, and 29 mm) of CoreValve (CV) were tested on a pulse duplicator in annulus size ranging from 17 to 26 mm. Leaflet bending stress and bending of the leaflet coaptation line in diastole pinwheeling index (PI) were measured using high-speed camera imaging (1000 images/s). For each given CV and annulus size, geometric orifice area (GOA) increased significantly with OS (P < .001) and SV (P = .001). LBS decreased with increasing prosthesis size and aortic annulus (AA) size while increasing with SV (P < .03). The largest value of peak LBS (3.79 MPa) was obtained with the CV 23 mm in AA of 17 mm (%OS = 35%), SV 90 mL and the smallest value (0.99 MPa) for the CV 29 mm in AA of 26 mm (%OS = 12%), SV 30 mL. On multivariable analysis, LBS increased independently with larger OS, smaller AA size and higher SV. The PI increased with decreasing AA size and increasing OS. Moderate valve OS, such as generally used for transcatheter aortic valve implantation, is associated with increased LBS during valve opening and closing, especially in small annuli. Hence, TAV OS may negatively impact long-term valve durability.
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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 | 0.001 |
| 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 | 0.000 | 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".