Bioprosthetic valve fracture: Predictors of outcome and <scp>follow‐up</scp> . Results from a multicenter study
Bibliographic record
Abstract
OBJECTIVES: To evaluate outcome and its predictors of bioprosthetic valve fracture (BVF) in patients undergoing valve-in-valve transcatheter aortic valve replacement (VIV-TAVR). BACKGROUND: BVF is feasible and reduces transvalvular gradients in VIV-TAVR-procedures, but follow-up-data and information on factors influencing the outcome are missing. METHODS: The 81 cases of BVF-VIV-TAVR were collected from 14 international centers. RESULTS: Predominantly transcatheter heart valve (THV) was implanted first, followed by BVF. VARC-2 defined device success was 93%, most failures were attributed to residual high gradients. Mean gradients decreased from 37 ± 13 mmHg to 10.8 ± 5.9 mmHg (p < 0.001). BVF reduced the gradient by 16 mmHg. During follow-up (FU, 281 ± 164 days) mean gradient remained stable (10.8 ± 5.9 mmHg at discharge, 12.4 ± 6.3 mmHg at FU, p = ns). In-hospital major adverse events occurred in 3.7%. Event-free survival at 276 ± 237.6 days was 95.4%. The linear mixed model identified balloon-expandable valves (BEV), Mitroflow surgical valve, stenotic surgical bioprostheses and balloon only 1 mm larger than the true internal diameter of the surgical valve as predictors for higher gradients. CONCLUSIONS: BVF is safe and can significantly reduce gradients, which remain stable at FU. BEV, Mitroflow surgical valve, stenotic bioprostheses and balloon larger than the true internal diameter of the surgical valve of only 1 mm are predictors for higher final gradients.
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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 | 0.004 |
| 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".