Transcatheter Replacement of Failed Bioprosthetic Valves
Bibliographic record
Abstract
BACKGROUND: Transcatheter valve implantation inside failed bioprosthetic surgical valves (valve-in-valve [ViV]) may offer an advantage over reoperation. Supra-annular transcatheter valve position may be advantageous in achieving better hemodynamics after ViV. Our objective was to define targets for implantation that would improve hemodynamics after ViV. METHODS AND RESULTS: Cases from the Valve-in-Valve International Data (VIVID) registry were analyzed using centralized core laboratory assessment blinded to clinical events. Multivariate analysis was performed to identify independent predictors of elevated postprocedural gradients (mean ≥20 mm Hg). Optimal implantation depths were defined by receiver operating characteristic curve. A total of 292 consecutive patients (age, 78.9±8.7 years; 60.3% male; 157 CoreValve Evolut and 135 Sapien XT) were evaluated. High implantation was associated with significantly lower rates of elevated gradients in comparison with low implantation (CoreValve Evolut, 15% versus 34.2%; P=0.03 and Sapien XT, 18.5% versus 43.5%; P=0.03, respectively). Optimal implantation depths were defined: CoreValve Evolut, 0 to 5 mm; Sapien XT, 0 to 2 mm (0-10% frame height); sensitivities, 91.3% and 88.5%, respectively. The strongest independent correlate for elevated gradients after ViV was device position (high: odds ratio, 0.22; confidence interval, 0.1-0.52; P=0.001), in addition to type of device used (CoreValve Evolut: odds ratio, 0.5; confidence interval, 0.28-0.88; P=0.02) and surgical valve mechanism of failure (stenosis/mixed baseline failure: odds ratio, 3.12; confidence interval, 1.51-6.45; P=0.002). CONCLUSIONS: High implantation inside failed bioprosthetic valves is a strong independent correlate of lower postprocedural gradients in both self- and balloon-expandable transcatheter valves. These clinical evaluations support specific implantation targets to optimize hemodynamics after ViV.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".