1-Year Outcomes following Bioprosthetic Valve Fracture to Facilitate Valve-in-Valve Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Background Bioprosthetic valve fracture (BVF) improves the hemodynamic results of valve-in-valve transcatheter aortic valve replacement (VIV TAVR) by facilitating optimal expansion of the transcatheter heart valve (THV). Long-term outcomes following BVF are unknown. Methods Consecutive cases of VIV TAVR and BVF (n = 139) performed at 11 sites were analyzed retrospectively. Hemodynamic measurements and aortic valve area (AVA) were assessed during the procedure and by echocardiography at 30-day and 1-year follow-up. Results VIV TAVR and BVF resulted in significant improvements in mean valve gradient (42.3 ± 17.1 vs. 9.4 ± 5.8 mmHg, p < 0.001) and AVA (0.8 ± 0.4 vs. 1.8 ± 0.7 cm 2 , p < 0.001) compared with baseline. Mortality was 2.3% at 30 days and 8.7% at 1-year. In adjusted models, mean valve gradient was higher (+5.1 [3.7, 6.5] mmHg, p < 0.001) and AVA was lower (−0.3 [−0.4, −0.2] cm 2 , p < 0.001) at 1 month as compared to post-procedure. Between 30 days and 1 year, no significant changes in mean valve gradient (+1.4 [−0.5, 3.4] mmHg, p = 0.15) or AVA (−0.1 [−0.3, 0.03] cm 2 , p = 0.11) were observed. In a multivariable analysis, use of a CoreValve (compared with a SAPIEN) THV was an independent predictor of a lower mean valve gradient at 1 year (−6.0 mmHg, p = 0.01). Conclusion Survival is excellent following VIV TAVR and BVF and valve hemodynamics are stable between 30-day and 1-year follow-up. CoreValve use is a predictor of better hemodynamic results following VIV TAVR and BVF. Abbreviations: VIV TAVR: valve-in-valve transcatheter aortic valve replacement; THV: transcatheter heart valve; BSV: bioprosthetic surgical valve; PPM: patient prosthesis mismatch; BVF: bioprosthetic valve fracture; IQR: interquartile range; LVEF: left ventricular ejection fraction; AVA: aortic valve area; STS PROM: Society of Thoracic Surgeons predicted risk of mortality
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".