title: Predictors of reintubation in critically ill patients.
abstract: BACKGROUND
  Assessment of a patient's readiness for removal of the endotracheal tube in the ICU is based on respiratory, airway, and neurological measures. However, nearly 20% of patients require reintubation. We created a prediction model for the need for reintubation, which incorporates variables importantly contributing to extubation failure.
  
  
  METHODS
  This was a cohort study of 2,007 endotracheally intubated subjects who required ICU admission at a tertiary care center. Data collection included demographic, hemodynamic, respiratory, and neurological variables preceding extubation. Data were compared between subjects extubated successfully and those who required reintubation, using bivariate logistic regression models, with the binary outcome reintubation and the baseline characteristics as predictors. Multivariable logistic regression analysis with robust variance was used to build the prediction model.
  
  
  RESULTS
  Of the 2,007 subjects analyzed, 376 (19%) required reintubation. In the bivariate analysis, admission Simplified Acute Physiology Score II, minute ventilation, breathing frequency, oxygenation, number of prior SBTs, rapid shallow breathing index, airway-secretions suctioning frequency and quantity, heart rate, and diastolic blood pressure differed significantly between the extubation success and failure groups. In the multivariable analysis, higher Simplified Acute Physiology Score II and suctioning frequency were associated with failed extubation. The area under the receiver operating characteristic curve was 0.68 for failure at any time, and 0.71 for failure within 24 hours. However, prior failed SBT, minute ventilation, and diastolic blood pressure were additional independent predictors of failure at any time, whereas oxygenation predicted extubation failure within 24 hours.
  
  
  CONCLUSIONS
  A small number of independent variables explains a substantial portion of the variability of extubation failure, and can help identify patients at high risk of needing reintubation. These characteristics should be incorporated in the decision-making process of ICU extubation.
authors.affiliation: Department of Anesthesiology and Pain Medicine, Harborview Medical Center.
authors.forename: Timothy
authors.initials: T
authors.lastname: Miu
authors.forename: Aaron M
authors.initials: AM
authors.lastname: Joffe
authors.forename: N David
authors.initials: ND
authors.lastname: Yanez
authors.forename: Nita
authors.initials: N
authors.lastname: Khandelwal
authors.forename: Armagan Hc
authors.initials: AH
authors.lastname: Dagal
authors.forename: Steven
authors.initials: S
authors.lastname: Deem
authors.forename: Miriam M
authors.initials: MM
authors.lastname: Treggiari
mesh_terms: D018806:APACHE; D000328:Adult; D015331:Cohort Studies; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D007442:Intubation, Intratracheal; D016015:Logistic Models; D008297:Male; D011379:Prognosis; D012372:ROC Curve; D012121:Respiration, Artificial; D019233:Retreatment; D012307:Risk Factors
keywords: ICU; airway management; clinical prediction model; cohort study; extubation; human; mechanical ventilation