Early Detection of Risk of Neo-Sinus Blood Stasis Post-Transcatheter Aortic Valve Replacement Using Personalized Hemodynamic Analysis
Bibliographic record
Abstract
Background: Despite the demonstrated benefits of transcatheter aortic valve replacement (TAVR), subclinical leaflet thrombosis and hypoattenuated leaflet thickening are commonly seen as initial indications of decreased valve durability and augmented risk of transient ischemic attack. Methods: We developed a multiscale patient-specific computational framework to quantify metrics of global circulatory function, metrics of global cardiac function, and local cardiac fluid dynamics of the aortic root and coronary arteries. Results: Based on our findings, TAVR might be associated with a high risk of blood stagnation in the neo-sinus region due to the lack of sufficient blood flow washout during the diastole phase (e.g., maximum blood stasis volume increased by 13, 8, and 2.7 fold in the left coronary cusp, right coronary cusp, and noncoronary cusp, respectively [N = 26]). Moreover, in some patients, TAVR might not be associated with left ventricle load relief (e.g., left ventricle load reduced only by 1.2 % [N = 26]) and diastolic coronary flow improvement (e.g., maximum coronary flow reduced by 4.94%, 15.05%, and 23.59% in the left anterior descending, left circumflex coronary artery, and right coronary artery, respectively, [N = 26]). Conclusions: The transvalvular pressure gradient amelioration after TAVR might not translate into adequate sinus blood washout, optimal coronary flow, and reduced cardiac stress. Noninvasive personalized computational modeling can facilitate the determination of the most effective revascularization strategy pre-TAVR and monitor leaflet thrombosis and coronary plaque progression post-TAVR.
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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) | 0.001 | 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".