Long-Term Outcomes After Edge-to-Edge Repair of Secondary Mitral Regurgitation
Bibliographic record
Abstract
BACKGROUND: Mitral valve transcatheter edge-to-edge repair (M-TEER) reduces secondary mitral regurgitation (MR) in heart failure and impacts survival in selected patients as demonstrated in the COAPT (Cardiovascular Outcomes Assessment of the MitraClip Percutaneous Therapy for Heart Failure Patients with Functional Mitral Regurgitation) trial. However, long-term outcome data after M-TEER under real-world conditions are lacking. OBJECTIVES: This study sought to assess long-term efficacy and survival after M-TEER in a large real-world registry. METHODS: We analyzed patients with significant secondary MR undergoing M-TEER from the EuroSMR (European Registry of Transcatheter Repair for Secondary Mitral Regurgitation) registry. Long-term MR reduction, functional outcomes, survival rate, and predictors for all-cause mortality were assessed. RESULTS: In this study, 1,628 patients undergoing M-TEER (mean age 73.8 years, mean EuroSCORE II [European System for Cardiac Operative Risk Evaluation II] 6.9%, 86.6% NYHA functional class ≥III) with available long-term data were included. Five-year survival was 35.0%. Long-term MR reduction (MR grade ≤2+: baseline 4.1%, discharge 92.2%, 5-year follow-up 85.5%; P < 0.001) and functional improvement (NYHA ≤II: baseline 13.4%, 5-year follow-up 60.1%; P < 0.001) was observed. The degree of residual MR was associated with 5-year survival (residual MR grade ≤1+: 38.6%; 2+: 30.5%; ≥3+: 22.6%; P < 0.001). Independent predictors for 5-year all-cause mortality post-M-TEER included age, renal function, residual MR, NYHA functional class, left ventricular ejection-fraction, and COAPT trial eligibility (P < 0.01 for all). CONCLUSIONS: This extensive multicenter registry underscores the long-term efficacy of M-TEER in real-world clinical practice and identifies predictors for long-term survival. These findings contribute valuable insights for optimizing patient selection in routine clinical interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".