Predictors of prolonged mechanical ventilation in a cohort of 5123 cardiac surgical patients
Bibliographic record
Abstract
BACKGROUND: Prolonged mechanical ventilation (PMV) after heart surgery is associated with increased patient morbidity and mortality. METHODS: In this prospective observational cohort study the aim was to assess PMV predictors and its impact on ICU, hospital length of stay and survival in cardiac surgical patients admitted to our eight-bed ICU from January 2000 to December 2006. All perioperative patient variables were put into an electronic database. Five thousand one hundred and twenty-three patients were divided into two cohorts: early extubation, undergoing a successful extubation for 12 h or less, and delayed extubation, needing a mechanical ventilation for more than 12 h. RESULTS: A logistic regression model identified the following as PMV predictors: age more than 65 years [odds ratio (OR), 1.296; 95% confidence interval (CI), 1.017-1.069; P = 0.016], chronic renal failure (OR, 1.571; 95% CI, 1.566-2.466; P = 0.011), chronic obstructive pulmonary disease (OR, 1.453; 95% CI, 1.695-2.454; P = 0.006), redo surgery (OR, 2.010; 95% CI, 1.389- 2.114; P = 0.001), emergency surgery (OR, 1.622; 95% CI, 1.515-2.494; P = 0.016), New York Heart Association/Canadian Cardiovascular Society class higher than 2 (OR, 1.491; 95% CI, 1.704-2.321; P = 0.001), left ventricular ejection fraction of 30% or less (OR, 2.125; 95% CI, 1.379-1.991; P = 0.000), red blood cell (OR, 5.430; 95% CI, 3.636-8.130; P = 0.000) and fresh frozen plasma transfusion units more than four (OR, 3.019; 95% CI, 1.808-5.050; P = 0.000) and cardiopulmonary bypass time more than 77 min (OR, 2.030; 95% CI, 1.248-2.174; P = 0.002). Early extubation group patients showed a higher probability of being discharged from ICU to cardiac surgical ward (log-rank = 1108.951; P = 0.000) and from cardiac to rehabilitation ward (log-rank = 598.005; P = 0.000) and higher hospital survival (log-rank = 53.215; P = 0.000). CONCLUSION: This review allowed us to assess predictors, helping us to identify 'a priori' patients more likely to undergo PMV.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".