Balloon versus self‐expandable transcatheter aortic valve implantation for bicuspid aortic valve stenosis: A meta‐analysis of observational studies
Bibliographic record
Abstract
BACKGROUND: There is a rising trend for transcatheter aortic valve implantation (TAVI) in bicuspid aortic stenosis patients. Data on the use of self-expandable (SEV) vs. balloon-expandable (BEV) valves in these patients are scarce. Therefore, we systematically compared clinical outcomes in bicuspid aortic stenosis patients treated with SEV and BEV. METHODS: Data were extracted from PubMed/MEDLINE, EMBASE, CENTRAL/CCTR, ClinicalTrials.gov, SciELO, LILACS, Google Scholar and reference lists of relevant articles. Eight studies published from 2013 to 2020 including a total of 1,080 patients (BEV: n = 620; SEV: n = 460) were selected. Primary endpoints were procedural, 30-day and 1-year mortality. Secondary endpoints were new pacemaker implantation, annular rupture, coronary obstruction, moderate-to-severe paravalvular leak, need of second valve, stroke and acute kidney injury. RESULTS: We found no statistically significant difference in mortality between patients treated with BEV vs. SEV during index procedure, at 30 days and at 1 year. BEVs showed a statistically significant higher risk of annulus rupture (2.5%) in comparison with SEV (0%) (OR 5.81 [95% CI, 3.78-8.92], p < .001). New generation BEVs were also associated with significantly less paravalvular leak when compared to new generation SEVs (OR 0.08 [95% CI, 0.02-0.35], p = .001). CONCLUSIONS: This meta-analysis of observational studies of TAVI for bicuspid valves, showed no difference in short- and mid-term TAVI mortality with BEVs and SEVs. BEVs presented a higher risk of annular rupture in comparison with SEV.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.025 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".