Stent and Leaflet Stresses of 29mm, First-generation, Self-expandable Transcatheter Aortic Valve Deployed in Patient Aortic Root
Bibliographic record
Abstract
Objective: Transcatheter aortic valve replacement (TAVR) is approved for low-, intermediate-, high-, and prohibitive-risk patients with severe aortic stenosis, but questions remain regarding long-term durability. We previously determined leaflet stresses for nominal ex-vivo TAVRs. Our goal here was to quantify TAVR stent and leaflet stresses when deployed in native patient anatomy to determine biomechanical interactions in patients. Methods: Commercial CoreValve underwent high-resolution micro-computed tomography (micro-CT) to develop precise geometries. Stent material properties were based on Nitinol, whereas leaflet materials were based on published literature of fixed porcine pericardium. Three patients’ pre-TAVR CT angiography (CTA) images were contoured with MeVisLab and imported into LS-DYNA. TAVR models underwent simulated deployment in patient-specific anatomy and loading at systemic pressurization; geometries were validated with post-TAVR CT geometries. Short- and long-term endpoints were collected from clinical follow-up and echocardiography. Results: At 80mmHg, 1st and 2nd principal stresses on the TAVR stent for patient-specific deployment were 377.7±101.8 MPa and 85.7±61.4 MPa, respectively, and 488.7±16.2 kPa and 236.8±8.9 kPa, respectively, on the leaflet (Figure). All three TAVR patients were alive without complications at 30-day and 1-year echocardiography follow-up. Conclusions: Patient-specific deployment demonstrated constraint of CoreValve stent shape based upon patient-specific anatomy, which in turn impacted leaflet geometry. Leaflet pinwheeling was demonstrated, but leaflet stresses in self-expanding TAVR were still less than that previously reported for balloon-expandable TAVR. TAVR leaflet stress distribution varied among the individual patient-specific models. Finite-element analysis of native aortic root morphologies is essential for analyzing the impact of stent distortion and leaflet function on long-term durability. KEYWORD: e-P-24 The authors do not declare any conflict of interest.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".