Transcatheter Mitral Valve Replacement or Repair for Secondary Mitral Regurgitation: A Propensity Score-Matched Analysis
Bibliographic record
Abstract
AIMS: This study aimed to compare outcomes after transcatheter mitral valve replacement (TMVR) and mitral valve transcatheter edge-to-edge repair (M-TEER) for the treatment of secondary mitral regurgitation (SMR). METHODS AND RESULTS: The CHOICE-MI registry included 262 patients with SMR treated with TMVR between 2014 and 2022. The EuroSMR registry included 1065 patients with SMR treated with M-TEER between 2014 and 2019. Propensity score (PS) matching was performed for 12 demographic, clinical and echocardiographic parameters. Echocardiographic, functional and clinical outcomes out to 1 year were compared in the matched cohorts. After PS matching, 235 TMVR patients (75.5 years [70.0, 80.0], 60.2% male, EuroSCORE II 6.3% [interquartile range 3.8, 12.4]) were compared to 411 M-TEER patients (76.7 years [70.1, 80.5], 59.0% male, EuroSCORE II 6.7% [3.9, 12.4]). All-cause mortality was 6.8% after TMVR and 3.8% after M-TEER at 30 days (p = 0.11), and 25.8% after TMVR and 18.9% after M-TEER at 1 year (p = 0.056). No differences in mortality after 1 year were found between both groups in a 30-day landmark analysis (TMVR: 20.4%, M-TEER: 15.8%, p = 0.21). Compared to M-TEER, TMVR resulted in more effective mitral regurgitation (MR) reduction (residual MR ≤1+ at discharge for TMVR vs. M-TEER: 95.8% vs. 68.8%, p < 0.001), and superior symptomatic improvement (New York Heart Association class ≤II at 1 year: 77.8% vs. 64.3%, p = 0.015). CONCLUSION: In this PS-matched comparison between TMVR and M-TEER in patients with severe SMR, TMVR was associated with superior reduction of MR and superior symptomatic improvement. While post-procedural mortality tended to be higher after TMVR, no significant differences in mortality were found beyond 30 days.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".