Seven-year outcomes following aortic valve replacement with a novel tissue bioprosthesis
Bibliographic record
Abstract
Objective As bioprosthetic aortic valve replacement (AVR) extends to younger cohorts, tissue durability is of paramount importance. We report 7-year outcomes from an AVR bioprosthesis utilizing novel tissue. Methods International Investigational Device Exemption trial for novel AVR with annual follow-up and a subset re-consented at 5 years for extended 10-year follow-up. Safety endpoints and echocardiographic measurements were adjudicated by an independent clinical events committee and by a dedicated core laboratory, respectively. Results Between 1/2013 and 3/2016, 689 patients underwent AVR with the study valve. Mean age was 66.9±11.6 years, STS risk score was 2.0±1.8%, and 74.3% of patients were NYHA Class II and III. Five-year follow-up was completed by 512 patients, and 225 reconsented for extended follow-up. Follow-up duration was 5.3±2.2 years (3,665.6 patient-years), and 194 and 195 patients completed 6- and 7-year follow-ups, respectively. One-, 5-, and 7-year freedom from all-cause mortality was 97.7%, 89.4%, and 85.4%, respectively. Freedom from structural valve deterioration at 7 years was 99.3%. At 7 years, effective orifice area and mean gradients were 1.82±0.57 cm 2 (n=153), and 9.4±4.5 mmHg (n=157), respectively. At 7 years, predominantly none (96.8%, n=152/157) or trivial/trace (2.5%, n=4/157) paravalvular regurgitation and none (84.7%, n=133/157) or trivial/trace (11.5%, n=18/157) transvalvular regurgitation were observed. Conclusions We report the longest surgical AVR follow-up with novel tissue in an IDE trial utilizing an independent clinical events committee and an echocardiography core laboratory. This tissue demonstrates excellent outcomes through 7 years and is the benchmark for future surgical and transcatheter prostheses.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".