Clinical Presentation and Outcomes After Surgery for Mitral Regurgitation: Real-World Insights From the MITRACURE International Registry
Bibliographic record
Abstract
BACKGROUND: Comprehensive knowledge of the clinical presentation, contemporary management, and outcomes on "all-comer" patients referred for mitral valve surgery (MVS) are critical to evaluate current practice and adherence to guidelines, understand selection biases, and inform key stakeholders on quality improvement. METHODS: MITRACURE is a large international retrospective registry of consecutive adult patients who underwent isolated or combined MVS for mitral regurgitation (MR) in France or Canada in 2019 with in-depth clinical and echocardiographic characterization. Patients operated on for isolated mitral stenosis or who had a prior mitral valve intervention were excluded. Data were obtained from detailed chart abstraction and were site reported. RESULTS: In 2019, 3522 patients underwent MVS (48% combined) across 40 centers (88±46 MVSs/center, median 80, interquartile [51-131]). Mean age was 65±12 years, and 35% were women. The most common MR etiology was myxomatous (61%), followed by functional (9%), infective endocarditis (9%), and rheumatic disease (7%). MR quantification was performed in only 43%. Advanced clinical presentation was common: 43% were in New York Heart Association class III/IV, 30% exhibited congestive heart failure, 47% were on diuretics, 22% had atrial fibrillation/flutter, 35% presented with reduced ejection fraction, and 22% had pulmonary hypertension (≥50 mm Hg). Most patients were symptomatic or presented with class I/IIa indication for intervention, and an early intervention was performed only in 3% of patients. The repair rate was 62% overall and 80% in myxomatous disease. In-hospital mortality was 4.5% overall but 2.3% in patients with myxomatous MR (1.8% isolated, 3.1% combined). CONCLUSIONS: MITRACURE provides a contemporary, multicenter, "real-world" picture of the clinical presentation, management, and in-hospital outcomes of MVS for MR in two Western countries. Patients were often referred late in the disease process, with few patients undergoing early intervention. The higher mortality and lower repair rates reported may be more reflective of an unselected MR patient population but have room for improvement. Our results underline the need to develop strategies to improve management and outcomes of patients with MR.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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".