Transcatheter aortic valve implantation versus redo surgery for failing surgical aortic bioprostheses: a multicentre propensity score analysis
Bibliographic record
Abstract
AIMS: Transcatheter aortic valve implantation for a failing surgical bioprosthesis (TAV-in-SAV) has become an alternative for patients at high risk for redo surgical aortic valve replacement (redo-SAVR). Comparisons between these approaches are non-existent. This study aimed to compare clinical and echocardiographic outcomes of patients undergoing TAV-in-SAV versus redo-SAVR after accounting for baseline differences by propensity score matching. METHODS AND RESULTS: Patients from seven centres in Europe and Canada who had undergone either TAV-in-SAV (n=79) or redo-SAVR (n=126) were identified. Significant independent predictors used for propensity scoring were age, NYHA functional class, number of prior cardiac surgeries, urgent procedure, pulmonary hypertension, and COPD grade. Using a calliper range of ±0.05, a total of 78 well-matched patient pairs were found. All-cause mortality was similar between groups at 30 days (6.4% redo-SAVR vs. 3.9% TAV-in-SAV; p=0.49) and one year (13.1% redo-SAVR vs. 12.3% TAV-in-SAV; p=0.80). Both groups also showed similar incidences of stroke (0% redo-SAVR vs. 1.3% TAV-in-SAV; p=1.0) and new pacemaker implantation (10.3% redo-SAVR vs. 10.3% TAV-in-SAV; p=1.0). The incidence of acute kidney injury requiring dialysis was numerically lower in the TAV-in-SAV group (11.5% redo-SAVR vs. 3.8% TAV-in-SAV; p=0.13). The TAV-in-SAV group had a significantly shorter median total hospital stay (12 days redo-SAVR vs. 9 days TAV-in-SAV; p=0.001). CONCLUSIONS: Patients with aortic bioprosthesis failure treated with either redo-SAVR or TAV-in-SAV have similar 30-day and one-year clinical outcomes.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".