Bioprosthetic Valve Fracture to Facilitate Valve-in-Valve Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Degeneration of surgical bioprosthetic heart valves (BPVs) occurs in up to one third of surviving patients within a decade of implantation and is a common cause of cardiovascular morbidity. Valve-in-valve transcatheter aortic valve replacement (VIV TAVR) is a safe and effective treatment for patients with failed BPVs who are unsuitable for reoperation. However, there may be patient-prosthesis mismatch (PPM) following VIV TAVR, particularly in patients with small BPVs. This may influence both morbidity and mortality. Bioprosthetic valve fracture (BVF) has emerged as a novel technique to prevent PPM. It involves high-pressure inflation of a non-compliant balloon to fracture the ring of the BPV, allowing for further expansion of the implanted transcatheter heart valve (THV) and thereby reducing residual transvalvular gradients. Early experience with this technique has been promising. This review will describe the procedural technique of BVF, explore the lessons learned from bench testing and early clinical experience, and discuss the limitations of the current literature as well as future directions.Abbreviations: ACn: ACURATE neo; BPV: Bioprosthetic heart valve; BVF: Bioprosthetic valve fracture; BVR: Bioprosthetic valve remodeling; EOA: Effective orifice area; NYHA: New York Heart Association; PPM: Patient-prosthesis mismatch; PWI: Pin-wheeling index; S3: Sapien 3; SAVR: Surgical aortic valve replacement; TAVR: Transcatheter aortic valve replacement; TPVR: Transcatheter pulmonary valve replacement; THV: Transcatheter heart valve; VIV: Valve-in-valve
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".