A Novel Morphological Classification to Guide Transcatheter Mitral Valve Edge-to-Edge Repair for Commissural Mitral Regurgitation
Bibliographic record
Abstract
BACKGROUND: Mitral commissural prolapse poses significant anatomical challenges that can hinder the effectiveness of transcatheter edge-to-edge repair (TEER). OBJECTIVES: The aim of this study was to estimate the safety and effectiveness of applying a novel morphological classification to guide TEER in patients with commissural degenerative mitral regurgitation (DMR). METHODS: In this prospective, multicenter study across 18 centers in China, we classified patients with severe commissural DMR into 4 morphological types through detailed echocardiographic analysis. Customized TEER strategies were applied accordingly. Procedural success, clinical outcomes, echocardiographic parameters, and quality of life were assessed over a follow-up period, with a median follow-up of 18 months (Q1-Q3: 15-21 months). RESULTS: Among 540 patients screened, 126 (23.3%) exhibited commissural involvement. Tailored TEER strategies were successfully applied to 68 patients, achieving a technical success rate of 100% (n = 68 of 68; 95% CI: 0.933-1.000) and a device success rate of 97.1% (n = 66 of 68, 95% CI: 0.888-0.992). The 1-year follow-up revealed that 94.1% (n = 64 of 68; 95% CI: 0.849-0.981) of patients had residual mitral regurgitation of grade ≤2+, with 82.4% (n = 56 of 68; 95% CI: 0.708-0.902) at grade ≤1+, and no major complications. Additionally, significant improvements were noted in left ventricular dimensions and functional status. CONCLUSIONS: Our results highlight the value of the morphological classification system in enhancing TEER for commissural DMR. By addressing specific anatomical challenges, this system promotes tailored interventions that optimize procedural success and improve patient outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".