title: Shock on admission day is the best predictor of prolonged mechanical ventilation in the ICU.
abstract: OBJECTIVE
  To determine the incidence of prolonged mechanical ventilation (PMV), which is associated with increased health-care costs and risks of adverse events, and to identify its early predictors.
  
  
  METHODS
  Retrospective cohort.
  
  
  METHODS
  A medical-surgical ICU in a university-affiliated hospital.
  
  
  METHODS
  All patients admitted to the ICU over 3 years who received mechanical ventilation (MV) for > 12 h.
  
  
  METHODS
  None.
  
  
  METHODS
  PMV was defined as MV lasting > 21 days. We recorded epidemiologic data, severity scores, worst Pao(2)/fraction of inspired oxygen (Fio(2)), presence of shock on ICU admission day, cause for MV, length of MV, ICU length of stay (LOS), and hospital LOS. PMV patients were compared to patients weaned before 21 days (non-PMV group) to determine predictors of PMV.
  
  
  RESULTS
  Of 551 hospital admissions, 319 patients (58%) required MV > 12 h. One hundred thirty patients died early and were excluded. Seventy-nine patients (14%) required PMV. The non-PMV group consisted of 110 patients. Simplified acute physiology score (SAPS) II, APACHE (acute physiology and chronic health evaluation) II, therapeutic intervention scoring system, Pao(2)/Fio(2), shock, ICU LOS, and hospital LOS differed significantly between groups. However, logistic regression identified shock on ICU admission day as the only independent predictor of PMV (odds ratio, 3.10; p = 0.001). SAPS II and Pao(2)/Fio(2) had the nearest coefficients and were used to build the predictive model. Sensitivity analysis was performed including the 130 patients who died early, and shock remained the most powerful predictor.
  
  
  CONCLUSIONS
  PMV was a frequent event in this cohort. The presence of shock on ICU admission day was the only prognostic factor, even adjusting for severity of illness and hypoxemia.
authors.affiliation: Servicio de Terapia Investiva, Hospital Interzonal General de Agudos San Martín, 1900 La Plata, Buenos Aires, Argentina. elisaestenssoro@speedy.com.ar
authors.forename: Elisa
authors.initials: E
authors.lastname: Estenssoro
authors.forename: Francisco
authors.initials: F
authors.lastname: González
authors.forename: Enrique
authors.initials: E
authors.lastname: Laffaire
authors.forename: Héctor
authors.initials: H
authors.lastname: Canales
authors.forename: Gabriela
authors.initials: G
authors.lastname: Sáenz
authors.forename: Rosa
authors.initials: R
authors.lastname: Reina
authors.forename: Arnaldo
authors.initials: A
authors.lastname: Dubin
mesh_terms: D018806:APACHE; D000328:Adult; D000368:Aged; D001118:Argentina; D015331:Cohort Studies; D005260:Female; D006305:Health Status Indicators; D006785:Hospitals, University; D006801:Humans; D000860:Hypoxia / Q000453:epidemiology; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data*; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D012121:Respiration, Artificial / Q000706:statistics & numerical data; D012189:Retrospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D012769:Shock / Q000453:epidemiology*; D015300:Ventilator Weaning