Mitral valve repair versus replacement for endocarditis: A propensity-score matched analysis of early postoperative outcomes
Bibliographic record
Abstract
Objective We sought to compare early postoperative outcomes between mitral valve repair (MVr) and replacement (MVR) in patients with mitral valve endocarditis. Background The optimal surgical approach for mitral valve endocarditis remains controversial, with some studies suggesting that mitral valve repair may be associated with better outcomes than mitral valve replacement and others suggesting no significant differences. This study compares the early postoperative outcomes between repair and replacement in this cohort using a large national database. Methods A retrospective, propensity-score matched analysis was conducted using the national data from the United Kingdom, comparing 1381 patients who underwent repair and 3276 patients who underwent replacement between 2000 and 2019. The primary outcome was in-hospital mortality, and secondary outcomes included prolonged admission (>10 days), re-exploration for bleeding, postoperative stroke, and postoperative dialysis. Binary logistic regression models were conducted in the matched group to further examine the relationship between procedure and outcomes. Results After propensity-score matching, 1249 pairs were identified. In-hospital mortality was significantly lower in the repair group (4% vs 7%, p < .001). Rates of re-exploration for bleeding (6% vs 9%, p = .019), postoperative stroke (1% vs 3%, p = .030), and postoperative dialysis (5% vs 7%, p = .016) were also significantly lower in the repair group. Binary logistic regression analyses demonstrated repair to be independently associated with lower risk of both mortality (OR:0.65, 95% CI:0.43-0.97, p = .034) and re-exploration for bleeding (OR:0.70, 95% CI:0.51-0.96, p < .028). Conclusions This study suggests that patients receiving repair for mitral valve endocarditis have significantly lower mortality and better early postoperative outcomes than those receiving replacement.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".