Impact of mitral valve repair on late postoperative left ventricular function in patients with non-ischemic cardiomyopathy
Bibliographic record
Abstract
Purpose: The functional status of patients following mitral valve repair of severe mitral regurgitation (MR) with non-ischemic cardiomyopathy is not well described. Also, the change in postoperative left ventricular function and size following correction of MR in these patients is unknown. Methods: Between 2001 and 2012, 61 patients with non-ischemic cardiomyopathy underwent mitral valve repair of organic (N=37, 61%) or functional (N=24, 39%) MR. Mean patient age was 64.6±12.3 years and there were 21 females. Mean preoperative left ventricular ejection fraction (LVEF) was 26.2±5.2%. Clinical and echocardiographic follow-up extended to 10.7 years. Results: There were no perioperative deaths. Five-year freedom from recurrent congestive heart failure was 79.2±11.0% for patients following repair of functional MR and 72.5±10.0% for patients following repair of organic MR. Smaller preoperative indexed LV end-systolic diameter was associated with better freedom from recurrent congestive heart failure (hazard ratio 0.88±0.05, p=0.02). Overall, LV function improved in 29 (48%) patients. The mean change LVEF and indexed LV end-systolic diameter 2-years postoperatively was -0.9±21.3% and -2.7±6.2 mm/m2, respectively. Improvement in postoperative LV function occurred more often in younger patients (odds ratio for left ventricle dysfunction, OR, 0.95±0.02 per decreasing year) and in males (OR 0.2±0.4 for left ventricle dysfunction) (Both p<0.05). In follow-up, 2 patients underwent successful heart transplantation 1.9 and 7.9 years following mitral valve repair and 10 patients underwent automated implantable cardioverter-defibrillator placement. Five-year freedom from recurrent MR ≥3+ was 94.1±4.3%. Conclusions: Mitral valve repair can be performed with favorable outcomes in patients with non-ischemic cardiomyopathy. LV function improves in an important number of patients following surgery, particularly in young patients and in males.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".