Long-term outcomes comparison of mitral valve repair or replacement for secondary mitral valve regurgitation. An updated systematic review and reconstructed time-to-event study-level meta-analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: The ideal surgical intervention for secondary mitral regurgitation (SMR), a disease of the left ventricle not the mitral valve itself, is still debated. We performed an updated systematic review and study-level meta-analysis investigating mitral valve repair (MVr) versus mitral valve replacement (MVR) for adult patients with SMR, with or without coronary artery disease (CAD). METHODS: PubMed, CENTRAL and EMBASE were searched for studies comparing MVr versus MVR. Randomized trial or observational studies were considered eligible. Primary endpoint was long-term mortality for any cause. Kaplan-Meier survival curves were reconstructed and compared with Cox linear regression. Landmark analysis and time-varying hazard ratio (HR) were analyzed. Sensitivity analyses included meta-regression and separate sub-analysis. A random effects model was used. RESULTS: Twenty-three studies (MVr=3,727 and MVR=2,839) were included. One study was a randomized trial, and 19 studies were adjusted. The mean weighted follow-up was 3.7±2.8 years. MVR was associated with significative greater late mortality (HR=1.26; 95 % CI, 1.14-1.39; P<0.0001) at 10-year follow-up. There was a time-varying trend showing an increased risk of mortality in the first 2 years after MVR (HR=1.38; 95 % CI, 1.21-1.56; P<0.0001), after which this difference dissipated (HR=0.94; 95 % CI, 0.81-1.09; P=0.41). Separate sub-analyses showed comparable long-term mortality in patients with concomitant coronary surgery ≥90 %, left ventricle ejection fraction ≤40 %, and sub-valvular apparatus preservation rate of 100 %. CONCLUSIONS: Compared to repair, MVR is associated with higher probability of mortality in the first 2 years following surgery, after which the two procedures showed comparable late mortality rate.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.047 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".