Diastolic Dysfunction is Predictive of Difficult Weaning from Cardiopulmonary Bypass
Bibliographic record
Abstract
Diastolic function is receiving more attention since echocardiographic measurements were developed and have become widely available. The importance and significance of diastolic dysfunction (DD) observed before cardiac surgery and its relationship with adverse outcomes, such as difficult separation from cardiopulmonary bypass (CPB), have not been fully explored. In this study, we hypothesize that DD can be a predictor for the need of inotropic support to successfully separate from CPB. Ninety-two consecutive patients underwent surgery during the study period. Twenty-six patients were excluded. From the remaining 66 patients, 52 had coronary artery bypass grafting alone and 14 combined procedures, valvular surgery, and reoperations (redo). Systolic and diastolic function was evaluated by two experts blinded as to the clinical data except for the age. The evaluation of diastolic function was done according to published guidelines. The demographic, echocardiographic, and hemodynamic variables were entered in a logistic regression analysis to determine which variables were independent predictors of difficult separation from CPB and the need for postoperative vasoactive support. DD was present in 20 patients (30%). Patients with DD had lower weight (P = 0.046), less frequent coronary artery bypass grafting alone (P = 0.0004), more myocardial infarction before surgery (P = 0.02), higher regional wall motion score index (P = 0.0002), and larger left ventricle (P = 0.03). Total CPB time (P = 0.004) and ischemic time (P = 0.007) were longer in the DD group. Patients with DD required more frequent inotropic support at the end of surgery (P = 0.006) and up to 12 h after surgery (P = 0.003). Multivariate logistic regression identified female sex, DD, and total CPB time as predictive of difficult weaning and inotropic requirements up to 12 h after surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".