Mixed Valvular Disease Following Transcatheter Aortic Valve Replacement: Quantification and Systematic Differentiation Using Clinical Measurements and Image‐Based Patient‐Specific In Silico Modeling
Bibliographic record
Abstract
Background Mixed valvular disease (MVD), mitral regurgitation (MR) from pre‐existing disease in conjunction with paravalvular leak (PVL) following transcatheter aortic valve replacement (TAVR), is one of the most important stimuli for left ventricle (LV) dysfunction, associated with cardiac mortality. Despite the prevalence of MVD, the quantitative understanding of the interplay between pre‐existing MVD, PVL, LV, and post‐TAVR recovery is meager. Methods and Results We quantified the effects of MVD on valvular‐ventricular hemodynamics using an image‐based patient‐specific computational framework in 72 MVD patients. Doppler pressure was reduced by TAVR (mean, 77%; N=72; P <0.05), but it was not always accompanied by improvements in LV workload. TAVR had no effect on LV workload in 22 patients, and LV workload post‐TAVR significantly rose in 32 other patients. TAVR reduced LV workload in only 18 patients (25%). PVL significantly alters LV flow and increases shear stress on transcatheter aortic valve leaflets. It interacts with mitral inflow and elevates shear stresses on mitral valve and is one of the main contributors in worsening of MR post‐TAVR. MR worsened in 32 patients post‐TAVR and did not improve in 18 other patients. Conclusions PVL limits the benefit of TAVR by increasing LV load and worsening of MR and heart failure. Post‐TAVR, most MVD patients (75% of N=72; P <0.05) showed no improvements or even worsening of LV workload, whereas the majority of patients with PVL, but without that pre‐existing MR condition (60% of N=48; P <0.05), showed improvements in LV workload. MR and its exacerbation by PVL may hinder the success of 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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".