Abstract
OBJECTIVE: To derive a clinical prediction rule that uses bedside clinical variables to predict extubation failure (reintubation within 48 h) after a successful spontaneous breathing trial. METHODS: This prospective observational cohort study was performed at the Northwestern Memorial Hospital in Chicago, Illinois, which is a large tertiary-care university hospital. Among 673 consecutive patients who received mechanical ventilation during a 15-month period, 122 were ventilated for at least 2 days and did not undergo withdrawal of support or tracheostomy. These patients were followed after extubation to identify those who were reintubated within 48 h (extubation failure). We used logistic regression analysis to identify variables that predict reintubation, and we used bootstrap resampling to internally validate the predictors and adjust for overoptimism. RESULTS: Sixteen (13%) of the 122 patients required reintubation within 48 h. Three clinical variables predicted reintubation: moderate to copious endotracheal secretions (p = 0.001), Glasgow Coma Scale score ≤ 10 (p = 0.004), and hypercapnia (PaCO2 ≥ 44 mm Hg) during the spontaneous breathing trial (p = 0.001). Using logistic regression and bootstrap resampling to adjust for over-fitting, we derived a clinical prediction rule that combined those 3 clinical variables (area under the receiver operating characteristic curve 0.87, 95% confidence interval 0.74-0.94). CONCLUSIONS: With our clinical prediction rule that incorporates an assessment of mental status, endotracheal secretions, and pre-extubation P aCO2, clinicians can predict who will fail extubation despite a successful spontaneous breathing trial.
Original language | English (US) |
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Pages (from-to) | 1710-1717 |
Number of pages | 8 |
Journal | Respiratory Care |
Volume | 52 |
Issue number | 12 |
State | Published - Dec 1 2007 |
Keywords
- Endotracheal secretions
- Extubation failure
- Hypercapnia
- Mental status
- Weaning
ASJC Scopus subject areas
- Pulmonary and Respiratory Medicine
- Critical Care and Intensive Care Medicine