Aortic Valve Stenosis Alters Expression of Regional Aortic Wall Shear Stress: New Insights From a 4‐Dimensional Flow Magnetic Resonance Imaging Study of 571 Subjects
Bibliographic record
Abstract
Background Wall shear stress ( WSS ) is a stimulus for vessel wall remodeling. Differences in ascending aorta ( AA o) hemodynamics have been reported between bicuspid aortic valve ( BAV ) and tricuspid aortic valve patients with aortic dilatation, but the confounding impact of aortic valve stenosis ( AS ) is unknown. Methods and Results Five hundred seventy‐one subjects underwent 4‐dimensional flow magnetic resonance imaging in the thoracic aorta (210 right‐left BAV cusp fusions, 60 right‐noncoronary BAV cusp fusions, 245 tricuspid aortic valve patients with aortic dilatation, and 56 healthy controls). There were 166 of 515 (32%) patients with AS . WSS atlases were created to quantify group‐specific WSS patterns in the AA o as a function of AS severity. In BAV patients without AS , the different cusp fusion phenotypes resulted in distinct differences in eccentric WSS elevation: right‐left BAV patients exhibited increased WSS by 9% to 34% ( P <0.001) at the aortic root and along the entire outer curvature of the AA o whereas right‐noncoronary BAV patients showed 30% WSS increase ( P <0.001) at the distal portion of the AA o. WSS in tricuspid aortic valve patients with aortic dilatation patients with no AS was significantly reduced by 21% to 33% ( P <0.01) in 4 of 6 AA o regions. In all patient groups, mild, moderate, and severe AS resulted in a marked increase in regional WSS ( P <0.001). Moderate‐to‐severe AS further increased WSS magnitude and variability in the AA o. Differences between valve phenotypes were no longer apparent. Conclusions AS significantly alters aortic hemodynamics and WSS independent of aortic valve phenotype and over‐rides previously described flow patterns associated with BAV and tricuspid aortic valve with aortic dilatation. Severity of AS must be considered when investigating valve‐mediated aortopathy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".