Minimally Invasive Mitral Valve Surgery After Transcatheter Edge-to-Edge Repair
Bibliographic record
Abstract
Objective Up to 28% of patients may need mitral valve (MV) surgery after transcatheter edge-to-edge repair (TEER). This study evaluates the outcomes of minimally invasive MV surgery after TEER. Methods: International multicenter registry of minimally invasive MV surgery after TEER between 2013 and 2020. Subgroups were stratified by the number of devices implanted (≤1 vs >1), as well as time interval from TEER to surgery (≤1 year vs >1 year). Results: A total of 56 patients across 13 centers were included with a mean age of 73 ± 11 years, and 50% were female. The median Society of Thoracic Surgeons Predicted Risk of Mortality (STS PROM) score for MV replacement was 8% (Q1-Q3 = 5% to 11%) and the ratio of observed to expected mortality was 0.9. The etiology of mitral regurgitation (MR) prior to TEER was primary MR in 75% of patients and secondary MR in 25%. There were 30 patients (54%) who had >1 device implanted. The median time between TEER and surgery was 252 days (33 to 636 days). Hemodynamics, including MR severity, MV area, and mean gradient, significantly improved after minimally invasive surgery and sustained to 1-year follow-up. In-hospital and 30-day mortality was 7.1%, and 1-year actuarial survival was 85.6% ± 6%. Conclusions: Minimally invasive MV surgery after TEER may be achieved as predicted by the STS PROM. Most patients underwent MV replacement instead of repair. As TEER is applied more widely, patients should be informed about the potential need for surgical intervention over time after TEER. These discussions will allow better informed consent and post-procedure planning.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".