Differential Impact of Mitral Valve Repair on Outcome of Coronary Artery Bypass Grafting with or without Surgical Ventricular Reconstruction in the Surgical Treatment for Ischemic Heart Failure (STICH) Trial
Bibliographic record
Abstract
Background This study examined the impact of mitral valve repair (MVRe) on survival of patients with moderate or severe (≥2+) MR and ischemic cardiomyopathy randomized to coronary artery bypass grafting (CABG) versus CABG+surgical ventricular reconstruction (SVR) in the STICH trial. Methods Among patients with moderate or severe MR and ischemic cardiomyopathy undergoing CABG or CABG+SVR, the impact of MVRe on mortality between the two treatment arms was compared. Results Among 867 patients with assessment of baseline MR severity, 211 had moderate or severe MR. After excluding 7 patients who underwent mitral valve replacement, 50, 44, 62, and 48 patients underwent CABG, CABG+MVRe, CABG+SVR, and CABG+SVR+MVRe, respectively. Four-year mortality rates were lower following CABG+MVRe than CABG alone (16% vs. 55%; adjusted hazard ratio [HR] 0.30; 95% CI 0.13–0.71). In contrast, the CABG+SVR+MVRe and CABG+SVR groups had similar 4-year mortality of 39% vs. 39% (adjusted HR 0.88; 95% CI 0.46–1.70). MVRe had a more favorable effect on survival in patients undergoing CABG alone compared to CABG+SVR ( p = 0.013). Baseline MR severity was similar between patients that received CABG+MVRe and those that underwent CABG+SVR+MVRe. A larger proportion of patients demonstrated a reduction in MR between 4 and 24 months after CABG+MVRe compared to CABG+SVR+MVRe (50.0% versus 25.0%, p = 0.023). Conclusion In patients with moderate or severe MR and ischemic cardiomyopathy undergoing CABG, MVRe appears to have a favorable effect on survival. The addition of SVR to CABG may attenuate the anticipated benefits of MVRe by limiting the long-term reduction of MR with MVRe.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".