title: Prediction of death and prolonged mechanical ventilation in acute lung injury.
abstract: BACKGROUND
  Prediction of death and prolonged mechanical ventilation is important in terms of projecting resource utilization and in establishing protocols for clinical studies of acute lung injury (ALI). We aimed to identify risk factors for a combined end-point of death and/or prolonged ventilator dependence and developed an ALI-specific prediction model.
  
  
  METHODS
  In this retrospective analysis of three multicenter clinical studies, we identified predictors of death or ventilator dependence from variables prospectively recorded during the first three days of mechanical ventilation. After the prediction model was derived in an international cohort of patients with ALI, it was validated in two independent samples of patients enrolled in a clinical trial involving 17 academic centers and a North American population-based cohort.
  
  
  RESULTS
  A combined end-point of death and/or ventilator dependence at 14 days or later occurred in 68% of patients in the international cohort, 60% of patients in the clinical trial, and 59% of patients in the population-based cohort. In the derivation cohort, a model based on age, oxygenation index on day 3, and cardiovascular failure on day 3 predicted death and/or ventilator dependence. The prediction model performed better in the clinical trial validation cohort (area under the receiver operating curve 0.81, 95% confidence interval 0.77 to 0.84) than in the population-based validation cohort (0.71, 95% confidence interval 0.65 to 0.76).
  
  
  CONCLUSIONS
  A model based on age and cardiopulmonary function three days after the intubation is able to predict, moderately well, a combined end-point of death and/or prolonged mechanical ventilation in patients with ALI.
authors.affiliation: Mayo Clinic, 200 First Street SW, Rochester, Minnesota 55905, USA. gajic.ognjen@mayo.edu
authors.forename: Ognjen
authors.initials: O
authors.lastname: Gajic
authors.forename: Bekele
authors.initials: B
authors.lastname: Afessa
authors.forename: B Taylor
authors.initials: BT
authors.lastname: Thompson
authors.forename: Fernando
authors.initials: F
authors.lastname: Frutos-Vivar
authors.forename: Michael
authors.initials: M
authors.lastname: Malinchoc
authors.forename: Gordon D
authors.initials: GD
authors.lastname: Rubenfeld
authors.forename: André
authors.initials: A
authors.lastname: Esteban
authors.forename: Antonio
authors.initials: A
authors.lastname: Anzueto
authors.forename: Rolf D
authors.initials: RD
authors.lastname: Hubmayr
mesh_terms: D015331:Cohort Studies; D014943:Global Health; D006801:Humans; D016015:Logistic Models; D008962:Models, Theoretical; D012372:ROC Curve; D012121:Respiration, Artificial / Q000706:statistics & numerical data*; D012128:Respiratory Distress Syndrome / Q000401:mortality* / Q000628:therapy*; D012189:Retrospective Studies; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors