One-Year Outcomes of Early, Compassionate Use of the PASCAL Ace Implant System for Transcatheter Mitral Valve Repair
Bibliographic record
Abstract
Background Continued development of transcatheter mitral repair technologies is needed to address the large and diverse population of high-risk patients with symptomatic mitral regurgitation (MR). The new PASCAL Ace implant system, with its narrower profile, complements the original PASCAL transcatheter valve repair system. The aim of this study is to report 1-year outcomes from the early, compassionate-use observational experience with the novel PASCAL Ace implant system. Methods After heart team assessment, adults with symptomatic moderate-to-severe (3+) or severe (4+) MR despite optimal medical therapy were treated under compassionate use at 3 hospitals internationally. Data were prospectively collected, and outcomes were assessed over a 12-month follow-up period. Results Seventeen patients (mean age 76 years, 65% male, mean Society of Thoracic Surgeons Predicted Risk of Operative Mortality score 9.6) were treated. MR etiology was degenerative in 29%, functional in 65%, and mixed in 6%; 59% were in New York Heart Association (NYHA) class III-IV. Technical success was achieved in 100%, and procedural success in 94%. At 1 year, MR grade ≤2+ was achieved in 93% ( p < 0.001) with 88% survival rate and 94% free from heart failure hospitalization. The composite major adverse event rate was 6% and 100% of patients had ≤NYHA class II symptoms ( p < 0.001). Conclusions At 1 year, the PASCAL Ace implant system demonstrated feasibility in this early, compassionate use experience in a small group of symptomatic patients with anatomically complex MR. The unique features of the PASCAL Ace implant may expand the treatable MR population.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| 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".