Structural Valve Deterioration in Transcatheter Aortic Bioprostheses: Diagnosis, Pathogenesis, and Treatment
Bibliographic record
Abstract
Transcatheter aortic valve replacement has emerged as the preferred treatment modality in most patients with severe aortic stenosis. With its global adoption and broader application in younger and healthier patients, the issue of transcatheter bioprosthetic valve degeneration and its impact on valve durability continues to earn clinical relevance. Differences in the pathophysiologic processes that separate native from transcatheter heart valve deterioration remain poorly understood. When compared to surgical aortic bioprostheses, the mechanisms of valve degeneration are similar in transcatheter heart valves, with meaningful differences most noticeably found between the individual constructs of their design. Recognizing the clinical and hemodynamic presentation of structural valve degeneration remains paramount. The recently revised consensus guidelines that incorporate the integration of advanced multimodality imaging with invasive hemodynamics represent a major step forward in our ability to accurately diagnose bioprosthetic valve degeneration, and to identify differences in durability patterns, and to establish treatment recommendations for the lifetime management of patients with aortic stenosis. Parallel efforts to unmask the biomolecular differences in atherosclerotic plaque burden, valve calcification, and thrombotic diathesis, including host immunocompetence, between the different available bioprostheses, will further advance the role of emerging valve tissue technologies to improve durability. As with surgical heart valves, the optimal treatment options for redo-transcatheter aortic valve replacement and surgical explant remain poorly understood. Ongoing translational research in bench testing coupled with prospectively designed core lab-adjudicated clinical trials are much needed. This report provides a contemporary overview of transcatheter structural valve degeneration, including evolving concepts in its pathogenesis, diagnosis, and treatment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".