title: [Prolonged mechanical ventilation probability model].
abstract: OBJECTIVE
  To design a probability model for prolonged mechanical ventilation (PMV) using variables obtained during the first 24 hours of the start of MV.
  
  
  METHODS
  An observational, prospective, multicenter cohort study.
  
  
  METHODS
  Thirteen Spanish medical-surgical intensive care units.
  
  
  METHODS
  Adult patients requiring mechanical ventilation for more than 24 hours.
  
  
  METHODS
  None.
  
  
  METHODS
  APACHE II, SOFA, demographic data, clinical data, reason for mechanical ventilation, comorbidity, and functional condition. A multivariate risk model was constructed. The model contemplated a dependent variable with three possible conditions: 1. Early mortality; 2. Early extubation; and 3. PMV.
  
  
  RESULTS
  Of the 1661 included patients, 67.9% (n=1127) were men. Age: 62.1±16.2 years. APACHE II: 20.3±7.5. Total SOFA: 8.4±3.5. The APACHE II and SOFA scores were higher in patients ventilated for 7 or more days (p=0.04 and p=0.0001, respectively). Noninvasive ventilation failure was related to PMV (p=0.005). A multivariate model for the three above exposed outcomes was generated. The overall accuracy of the model in the training and validation sample was 0.763 (95%IC: 0.729-0.804) and 0.751 (95%IC: 0.672-0.816), respectively. The likelihood ratios (LRs) for early extubation, involving a cutoff point of 0.65, in the training sample were LR (+): 2.37 (95%CI: 1.77-3.19) and LR (-): 0.47 (95%CI: 0.41-0.55). The LRs for the early mortality model, for a cutoff point of 0.73, in the training sample, were LR (+): 2.64 (95%CI: 2.01-3.4) and LR (-): 0.39 (95%CI: 0.30-0.51).
  
  
  CONCLUSIONS
  The proposed model could be a helpful tool in decision making. However, because of its moderate accuracy, it should be considered as a first approach, and the results should be corroborated by further studies involving larger samples and the use of standardized criteria.
authors.affiliation: Servicio de Medicina Intensiva, Hospital Virgen de la Luz, Cuenca, España. jmaelizalde@gmail.com
authors.forename: J M
authors.initials: JM
authors.lastname: Añón
authors.forename: V
authors.initials: V
authors.lastname: Gómez-Tello
authors.forename: E
authors.initials: E
authors.lastname: González-Higueras
authors.forename: J J
authors.initials: JJ
authors.lastname: Oñoro
authors.forename: V
authors.initials: V
authors.lastname: Córcoles
authors.forename: M
authors.initials: M
authors.lastname: Quintana
authors.forename: J
authors.initials: J
authors.lastname: López-Martínez
authors.forename: L
authors.initials: L
authors.lastname: Marina
authors.forename: G
authors.initials: G
authors.lastname: Choperena
authors.forename: A M
authors.initials: AM
authors.lastname: García-Fernández
authors.forename: C
authors.initials: C
authors.lastname: Martín-Delgado
authors.forename: F
authors.initials: F
authors.lastname: Gordo
authors.forename: R
authors.initials: R
authors.lastname: Díaz-Alersi
authors.forename: J C
authors.initials: JC
authors.lastname: Montejo
authors.forename: A García de
authors.initials: AG
authors.lastname: Lorenzo
authors.forename: M
authors.initials: M
authors.lastname: Pérez-Arriaga
authors.forename: R
authors.initials: R
authors.lastname: Madero
mesh_terms: D015331:Cohort Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D011446:Prospective Studies; D012121:Respiration, Artificial; D013997:Time Factors