Long-term outcomes after transcatheter aortic valve implantation in failed bioprosthetic valves
Bibliographic record
Abstract
AIMS: Due to bioprosthetic valve degeneration, aortic valve-in-valve (ViV) procedures are increasingly performed. There are no data on long-term outcomes after aortic ViV. Our aim was to perform a large-scale assessment of long-term survival and reintervention after aortic ViV. METHODS AND RESULTS: A total of 1006 aortic ViV procedures performed more than 5 years ago [mean age 77.7 ± 9.7 years; 58.8% male; median STS-PROM score 7.3% (4.2-12.0)] were included in the analysis. Patients were treated with Medtronic self-expandable valves (CoreValve/Evolut, Medtronic Inc., Minneapolis, MN, USA) (n = 523, 52.0%), Edwards balloon-expandable valves (EBEV, SAPIEN/SAPIEN XT/SAPIEN 3, Edwards Lifesciences, Irvine, CA, USA) (n = 435, 43.2%), and other devices (n = 48, 4.8%). Survival was lower at 8 years in patients with small-failed bioprostheses [internal diameter (ID) ≤ 20 mm] compared with those with large-failed bioprostheses (ID > 20 mm) (33.2% vs. 40.5%, P = 0.01). Independent correlates for mortality included smaller-failed bioprosthetic valves [hazard ratio (HR) 1.07 (95% confidence interval (CI) 1.02-1.13)], age [HR 1.21 (95% CI 1.01-1.45)], and non-transfemoral access [HR 1.43 (95% CI 1.11-1.84)]. There were 40 reinterventions after ViV. Independent correlates for all-cause reintervention included pre-existing severe prosthesis-patient mismatch [subhazard ratio (SHR) 4.34 (95% CI 1.31-14.39)], device malposition [SHR 3.75 (95% CI 1.36-10.35)], EBEV [SHR 3.34 (95% CI 1.26-8.85)], and age [SHR 0.59 (95% CI 0.44-0.78)]. CONCLUSIONS: The size of the original failed valve may influence long-term mortality, and the type of the transcatheter valve may influence the need for reintervention after aortic 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".