Predictors of hospital mortality after surgery for ischemic mitral regurgitation
Bibliographic record
Abstract
BACKGROUND: The benefit of mitral valve repair over replacement in patients with ischemic mitral regurgitation is still controversial. We report our early postoperative outcomes of repair versus replacement. METHODS: Data were collected for patients undergoing first-time mitral valve surgery for ischemic mitral regurgitation between 1990 and 2009 (n = 393). Patients who underwent combined procedures for papillary muscle rupture, post-infarction ventricular septal defect, endocarditis, or any previous cardiac surgery were excluded. Preoperative demographics, operative variables, and hospital outcomes were analyzed, and multivariable regression analysis was employed to identify independent predictors of hospital mortality. RESULTS: Valve repair was performed in 42% (n=164) of patients and replacement in 58% (n=229). Patients who underwent replacement were older and had a higher prevalence of unstable angina, New York Heart Association class IV symptoms, preoperative cardiogenic shock, preoperative myocardial infarction, peripheral vascular disease, renal failure, and urgent or emergency surgery (all p < 0.05). Unadjusted hospital mortality was higher in patients undergoing valve replacement (13% versus 5%, p = 0.01). Valve repair was associated with a lower prevalence of postoperative low cardiac output syndrome. Multivariable analysis revealed that age, urgency of operation, and preoperative left ventricular function were independent predictors of hospital mortality. Importantly, mitral valve repair versus replacement was not an independent predictor of hospital mortality. CONCLUSION: Our data did not suggest an early survival benefit to mitral valve repair over replacement for ischemic mitral regurgitation. However, age, left ventricular dysfunction, and the need for urgent surgery were independently associated with hospital mortality.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".