Five-Year Outcomes After Bicuspid Aortic Valve Replacement With a Novel Tissue Bioprosthesis
Bibliographic record
Abstract
BACKGROUND: This study investigated the safety and effectiveness of surgical aortic valve replacement with RESILIA tissue (Edwards Lifesciences) through 5 years in patients with native bicuspid aortic valves. Outcomes were compared with those for patients with tricuspid aortic valves. METHODS: Of 689 patients from the COMMENCE (ProspeCtive, nOn-randoMized, MulticENter) trial who received the study valve, 645 had documented native valve morphology and core laboratory-evaluable echocardiograms from any postoperative visit, which were used to model hemodynamic outcomes over 5 years. Linear mixed-effects models were used to estimate longitudinal changes in mean gradient and effective orifice area. RESULTS: Patients with native bicuspid aortic valves (n = 214) were more than a decade younger than those with tricuspid aortic valves (n = 458; 59.8 ± 12.4 years vs 70.2 ± 9.5 years; P < .001). The bicuspid aortic valve cohort exhibited no structural valve deterioration over 5 years, and rates of paravalvular leak and transvalvular regurgitation were low (0.7% and 2.9%, respectively [all mild] at 5 years). These outcomes mirrored those in patients with native tricuspid aortic valves. The model-estimated postoperative mean gradient and effective orifice area, as well as the rate of change of these outcomes, adjusted for age, body surface area, and bioprosthesis size, did not differ between the 2 cohorts. CONCLUSIONS: Among patients with bicuspid aortic valves, RESILIA tissue valves demonstrated excellent outcomes to 5 years, including no structural valve deterioration and very low rates of paravalvular and transvalvular regurgitation. These results are encouraging for RESILIA tissue durability in young patients.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".