Preoperative prediction of prolonged mechanical ventilation following coronary artery bypass grafting
Bibliographic record
Abstract
OBJECTIVE: Few studies have attempted to evaluate who would require prolonged mechanical ventilation following heart surgery. The objectives of this study were to identify predictors of prolonged ventilation in a large group of coronary artery bypass grafting (CABG) patients from a single institution. METHODS: One thousand, eight hundred and twenty-nine consecutive patients undergoing CABG were reviewed retrospectively and evaluated for preoperative predictors of prolonged ventilation which included: age, gender, ejection fraction (EF), renal function, diabetes, angina status, New York Heart Association Class, number of diseased vessels, urgency of the procedure, re-operation, chronic lung disease (COPD) and intraoperative variables such as IABP, inotropes, stroke and myocardial infarction. Prolonged ventilation was defined as > or = 24 h. Stepwise logistic regression analysis was performed. RESULTS: Patients were on average 65.4+/-10.6 years of age, 30% were diabetic, 80% had triple vessel disease and 93% were of functional class III/IV. The mean ejection fraction was 60+/-16 percent. Overall peri-operative mortality was 2.7%. There were 157 patients that required prolonged ventilation with a peri-operative mortality of 18.5% (P < 0.001). Preoperative independent predictors of prolonged ventilation were found to be: unstable angina (OR 5.6), EF < 50 (OR 2.3), COPD (OR 2.0), preop. renal failure (OR 1.9), female gender (OR 1.8) and age > 70 (OR 1.7). Based on these predictors, a model was created to estimate of the risk of prolonged ventilation in individual patients following CABG with results ranging from < or = 3% in patients without any risk factors to > or = 32% in patients with five or more independent risk factors. Certain intraoperative variables were strong predictors of prolonged ventilation and included: stroke (OR 12.3), re-operation for bleeding (OR 6.9) and perioperative MI (OR 5.8). CONCLUSION: We were able to create a stable model where several preoperative and intra-operative variables were shown to be predictive of prolonged ventilation after CABG surgery. The ability to identify patients at increased risk for prolonged ventilation may allow the development of pre-emptive strategies and more effective resource allocation.
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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 | 0.005 |
| 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) | 0.002 | 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".