Myocardial Performance Index as Predictor of Adverse Outcomes Following Mitral Valve Surgery
Bibliographic record
Abstract
Aims: We aim to determine whether the myocardial performance index, will be a good predictor of adverse outcomes following mitral valve surgery. Method: We prospectively measured pre-operative myocardial performance index in 22 consecutive patients, with moderate to severe mitral insufficiency, undergoing corrective mitral valve surgery. The primary endpoint was predefined as either peri-operative death or congestive heart failure. Results: The primary endpoint occurred in nine patients. Five of the six patients with myocardial performance index ≥0·7 had primary endpoints. Chi-square testing demonstrated that the primary endpoint was significantly associated with advanced age (≥70 years) and myocardial performance index ≥0·7 ( P =0·003 and 0·01 respectively). There was a trend towards significant association of depressed left ventricle ejection fraction (left ventricle ejection fraction ≤40%) and the primary endpoint ( P =0·09). Although left ventricle ejection fraction ≤40% was more sensitive in predicting the primary endpoint, it has lower specificity, accuracy and predictive values than myocardial performance index ≥0·7. Conclusion: Our results suggest that myocardial performance index is a potentially useful predictor of increased risk of peri-operative death or congestive heart failure, in patients with moderate–severe mitral insufficiency undergoing corrective mitral valve surgery. In conjunction with left ventricle ejection fraction, it may be helpful in the pre-operative prognostication of these patients. Copyright 2002 The European Society of Cardiology, Published by Elsevier Science Ltd. All rights reserved.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.021 |
| 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 | 0.000 |
| 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; a candidate call from one teacher head, 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".