Hemodynamic and Clinical Outcomes in Redo-Surgical Aortic Valve Replacement vs. Transcatheter Valve-in-Valve
Bibliographic record
Abstract
Background Transcatheter valve-in-valve replacement (ViV-TAVR) has emerged as an alternative to redo-surgical aortic valve replacement (Redo-SAVR) for the treatment of failed surgical aortic bioprostheses. However, the benefit of ViV-TAVR compared with Redo-SAVR remains debated with regard to short-term hemodynamic results and short- and long-term clinical outcomes. Objective This study aimed to compare short-term hemodynamic performance and long-term clinical outcomes of ViV-TAVR vs. Redo-SAVR in patients treated for surgical aortic bioprosthetic valve failure. Methods We retrospectively analyzed the data prospectively collected in 184 patients who underwent Redo-SAVR or ViV-TAVR. Transthoracic echocardiography was performed before and after the procedure and analyzed in an echocardiography core laboratory using the new Valve Academic Research Consortium-3 criteria. An inverse probability of treatment weighting was used to compare the outcomes between both procedures. Results ViV-TAVR showed lower rate of intended hemodynamic performance (39.2% vs. 67.7%, p < 0.001) at 30 days, which was essentially driven by a higher rate (56.2% vs. 28.8%, p = 0.001) of high residual gradient (mean transvalvular gradient ≥20 mm Hg). Despite a trend for higher 30-day mortality in the Redo-SAVR vs. ViV-TAVR group (8.7% vs. 2.5%, odds ratio [95% CI]: 3.70 [0.77-17.6]; p = 0.10), the long-term mortality was significantly lower (24.2% vs. 50.1% at 8 years; hazard ratio [95% CI]: 0.48 [0.26-0.91]; p = 0.03) in the Redo-SAVR group. After inverse probability of treatment weighting analysis, Redo-SAVR remained significantly associated with reduced long-term mortality compared with ViV-TAVR (hazard ratio [95% CI]: 0.32 [0.22-0.46]; p < 0.001). Conclusions ViV-TAVR was associated with a lower rate of intended hemodynamic performance and numerically lower mortality at 30 days but higher rates of long-term mortality compared with Redo-SAVR.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".