MitraClip After Failed Surgical Mitral Valve Repair—An International Multicenter Study
Bibliographic record
Abstract
BACKGROUND: Recurrence of mitral regurgitation (MR) after surgical mitral valve repair (SMVR) varies and may require reoperation. Redo mitral valve surgery can be technically challenging and is associated with increased risk of mortality and morbidity. We aimed to assess the feasibility and safety of MitraClip as a treatment strategy after failed SMVR and identify procedure modifications to overcome technical challenges. METHODS AND RESULTS: This international multicenter observational retrospective study collected information for all patients from 16 high-volume hospitals who were treated with MitraClip after failed SMVR from October 29, 2009, until August 1, 2017. Data were anonymously collected. Technical and device success were recorded per modified Mitral Valve Academic Research Consortium criteria. Overall, 104 consecutive patients were included. Median Society of Thoracic Surgeons score was 4.5% and median age was 73 years. At baseline, the majority of patients (82%) were in New York Heart Association class ≥III and MR was moderate or higher in 86% of patients. The cause of MR pre-SMVR was degenerative in 50%, functional in 35%, mixed in 8%, and missing/unknown in 8% of patients. The median time between SMVR and MitraClip was 5.3 (1.9-9.7) years. Technical and device success were 90% and 89%, respectively. Additional/modified imaging was applied in 21% of cases. An MR reduction of ≥1 grade was achieved in 94% of patients and residual MR was moderate or less in 90% of patients. In-hospital all-cause mortality was 2%, and 86% of patients were in New York Heart Association class ≤II. CONCLUSIONS: MitraClip is a safe and less invasive treatment option for patients with recurrent MR after failed SMVR. Additional/modified imaging may help overcome technical challenges during leaflet grasping.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".