Incidence, predictors and clinical outcomes of residual stenosis after aortic valve-in-valve
Bibliographic record
Abstract
OBJECTIVE: We aimed to analyse the incidence of prosthesis-patient mismatch (PPM) and elevated gradients after aortic valve in valve (ViV), and to evaluate predictors and associations with clinical outcomes of this adverse event. METHODS: A total of 910 aortic ViV patients were investigated. Elevated residual gradients were defined as ≥20 mm Hg. PPM was identified based on the indexed effective orifice area (EOA), measured by echocardiography, and patient body mass index (BMI). Moderate and severe PPM (cases) were defined by European Association of Cardiovascular Imaging (EACVI) criteria and compared with patients without PPM (controls). RESULTS: Moderate or greater PPM was found in 61% of the patients, and severe in 24.6%. Elevated residual gradients were found in 27.9%. Independent risk factors for the occurrence of lower indexed EOA and therefore severe PPM were higher gradients of the failed bioprosthesis at baseline (unstandardised beta -0.023; 95% CI -0.032 to -0.014; P<0.001), a stented (vs a stentless) surgical bioprosthesis (unstandardised beta -0.11; 95% CI -0.161 to -0.071; P<0.001), higher BMI (unstandardised beta -0.01; 95% CI -0.013 to -0.007; P<0.001) and implantation of a SAPIEN/SAPIEN XT/SAPIEN 3 transcatheter device (unstandardised beta -0.064; 95% CI -0.095 to -0.032; P<0.001). Neither severe PPM nor elevated gradients had an association with VARC II-defined outcomes or 1-year survival (90.9% severe vs 91.5% moderate vs 89.3% none, P=0.44). CONCLUSIONS: Severe PPM and elevated gradients after aortic ViV are very common but were not associated with short-term survival and clinical outcomes. The long-term effect of poor post-ViV haemodynamics on clinical outcomes requires further evaluation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".