Long-Term Outcomes After Transcatheter Aortic Valve-in-Valve Replacement
Bibliographic record
Abstract
Background: Data on long-term outcomes after valve-in-valve (ViV) transcatheter aortic valve replacement (TAVR) are scarce. The objective of this study was to determine the long-term clinical outcomes and structural valve degeneration (SVD) over time in patients undergoing ViV-TAVR. Methods and Results: Consecutive patients undergoing ViV-TAVR in 9 centers between 2009 and 2015 were included. Patients were followed yearly, and clinical and echocardiography data were collected prospectively. SVD was defined as subclinical (increase >10 mm Hg in mean transvalvular gradient+decrease >0.3 cm 2 in valve area or new-onset mild or moderate aortic regurgitation) and clinically relevant (increase >20 mm Hg in mean transvalvular gradient+decrease >0.6 cm 2 in valve area or new-onset moderate-to-severe aortic regurgitation). A total of 116 patients (mean age, 76±11 years; 64.7% male; mean Society of Thoracic Surgeons score, 8.0±5.1%) were included. Balloon- and self-expandable valves were used in 47.9% and 52.1% of patients, respectively, and 30-day mortality was 6.9%. At a median follow-up of 3 years (range, 2–7 years), 30 patients (25.9%) had died, 20 of them (17.2%) from cardiovascular causes. Average mean transvalvular gradients remained stable up to 5-year follow-up ( P =0.92), but clinically relevant SVD occurred in 3/99 patients (3.0%), and 15/99 patients (15.1%) had subclinical SVD. One patient with SVD had redo ViV-TAVR. Conclusions: About one-fourth of ViV-TAVR recipients had died after a median follow-up of 3 years. Overall valve hemodynamics remained stable over time and clinically relevant SVD was infrequent, but 1 out of 10 patients exhibited some degree of SVD.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".