Long-term echocardiographic data, mechanisms of failure, and reintervention outcomes of the Epic valve in mitral position—a large observational cohort
Bibliographic record
Abstract
OBJECTIVES: Long-term echocardiographic reports on mitral valve (MV) porcine xenograft bioprosthesis (Epic) are lacking, and postreintervention outcomes of failed Epic are unknown. We aimed to assess the mechanisms and independent predictors of Epic failures and to compare short- and mid-term outcomes according to reintervention type. METHODS: We included consecutive patients (n = 1397; mean age: 72 ± 8 years; 46% female; mean follow-up: 4.8 years) who received the Epic during mitral valve replacement (MVR) at our institution. Clinical, echocardiographic, reintervention, and outcomes data were retrieved from our prospective institution's database or government statistics. RESULTS: Gradients and effective orifice area of the Epic were stable over 5-years follow-up. A total of 70 (5%) patients had a MV reintervention at median follow-up of 3.0 (0.7-5.4) years due to prosthesis failure, by redo-MVR (n = 38; 54%), valve-in-valve (n = 19; 27%), paravalvular leak (PVL) closure (n = 12; 17%), or thrombectomy (n = 1). Mechanisms of failure were 27 (1.9%) structural valve deterioration (SVD; all leaflet tear); 16 (1.1%) non-SVD (15 PVL, 1 pannus); 24 (1.7%) endocarditis; and 4 (0.3%) thrombosis. Freedom from all-cause and SVD-related MV reintervention at 10 years are 88% and 92%, respectively. Independent predictors of reintervention were age, baseline atrial fibrillation, initial MV etiology, and moderate or greater PVL at discharge (all P ≤ .05). Comparison of redo-MVR and valve-in-valve revealed no significant difference in early outcomes or mid-term mortality (all P ≥ .16). CONCLUSIONS: The Epic Mitral valve has stable hemodynamics through 5 years and is associated with low incidence of SVD and reintervention, mostly due to endocarditis and leaflet tear without calcification. Reintervention type had no influence on early outcomes and mid-term mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".