Transcatheter Aortic Valve Replacement for Failed Surgical Bioprostheses: Insights from the PARTNER II Valve-in-Valve Registry on Utilizing Baseline Computed-Tomographic Assessment
Bibliographic record
Abstract
Background Residual stenosis is a major limitation of transcatheter aortic valve replacement inside failed surgical bioprostheses (valve-in-valve). Our aim was to evaluate whether pre-procedure CT assessment could identify cases at risk for having residual stenosis after the procedure. Methods Patients with failed surgical aortic bioprostheses were prospectively enrolled in the multicenter PARTNER II valve-in-valve registry. Core-lab assessment of echocardiographic and CT findings were utilized. Results A total of 84 patients that underwent pre-procedural CT were included in the current analysis with a median age of 79.9 ± 9.6 years with 65.5% being male. CT average annulus internal area was 331.64 ± 73.52mm 2 . Post SAPIEN XT implantation mean gradient was 17.95 ± 7.59 mmHg and average aortic valve area was 1.06 ± 0.35 cm 2 . Small internal annular area per CT was significantly associated with increased gradients in intermediate/large surgical valves (true ID > 20 mm, p = 0.01). ROC curve for the evaluation of predictability of CT measured area on post-procedural gradients in intermediate/large surgical valves was high (AUC 0.81). Cutoff of 329 mm 2 had negative predictive value of 95%. Conclusions CT-derived annulus area in cases with intermediate and large surgical valves can identify cases at risk for poor hemodynamics after valve-in-valve and influence clinical decision making.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".