Prosthesis‐patient mismatch after transcatheter implantation of contemporary balloon‐expandable and self‐expandable valves in small aortic annuli
Bibliographic record
Abstract
BACKGROUND: Evidence of clinical impact of PPM after TAVI is conflicting and might vary according to the type of valve implanted. AIMS: To assess the clinical impact of prosthesis-patient mismatch (PPM) after transcatheter aortic valve implantation (TAVI) with balloon-expandable (BEV) and self-expandable valves (SEV) in patients with small annuli. METHODS: ) treated with transfemoral TAVI at 16 high-volume centers between 2011 and 2020. Analyses were performed comparing patients with less than moderate (n = 452), moderate (n = 138), and severe PPM (n = 38). Primary endpoint was incidence of all-cause mortality. Predictors of all-cause mortality and PPM were investigated. RESULTS: At a median follow-up of 380 days (interquartile range: 210-709 days), patients with severe PPM, but not moderate PPM, had an increased risk of all-cause mortality when compared with less than moderate PPM (log-rank p = 0.046). Severe PPM predicted all-cause mortality in patients with BEV (hazard ratio [HR]: 5.20, 95% confidence interval [CI]: 1.27-21.2) and intra-annular valves (IAVs, HR: 4.23, 95% CI: 1.28-14.02), and it did so with borderline significance in the overall population (HR: 2.89, 95% CI: 0.95-8.79). Supra-annular valve (SAV) implantation was the only predictor of severe PPM (odds ratio: 0.33, 95% CI: 0.13-0.83). CONCLUSIONS: Patients with small aortic annuli and severe PPM after TAVI have an increased risk of all-cause mortality at early term follow-up, especially after IAV or BEV implantation. TAVI with SAV protected from severe PPM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".