title: Tracheostomy does not improve the outcome of patients requiring prolonged mechanical ventilation: a propensity analysis.
abstract: OBJECTIVE
  To examine the association between the performance of a tracheostomy and intensive care unit and postintensive care unit mortality, controlling for treatment selection bias and confounding variables.
  
  
  METHODS
  Prospective, observational, cohort study.
  
  
  METHODS
  Twelve French medical or surgical intensive care units.
  
  
  METHODS
  Unselected patients requiring mechanical ventilation for > or =48 hrs enrolled between 1997 and 2004.
  
  
  METHODS
  None.
  
  
  RESULTS
  Two models of propensity scores for tracheostomy were built using multivariate logistic regression. After matching on these propensity scores, the association of tracheostomy with outcomes was assessed using multivariate conditional logistic regression. Results obtained with the two models were compared. Of the 2,186 patients included, 177 (8.1%) received a tracheostomy. Both models led to similar results. Tracheostomy did not improve intensive care unit survival (model 1: odds ratio, 0.94; 95% confidence interval, 0.63-1.39; p = .74; model 2: odds ratio, 1.12; 95% confidence interval, 0.75-1.67; p = .59). There was no difference whether tracheostomy was performed early (within 7 days of ventilation) or late (after 7 days of ventilation). In patients discharged free from mechanical ventilation, tracheostomy was associated with increased postintensive care unit mortality when the tracheostomy tube was left in place (model 1: odds ratio, 3.73; 95% confidence interval, 1.41-9.83; p = .008; model 2: odds ratio, 4.63; 95% confidence interval, 1.68-12.72, p = .003).
  
  
  CONCLUSIONS
  Tracheostomy does not seem to reduce intensive care unit mortality when performed in unselected patients but may represent a burden after intensive care unit discharge.
authors.affiliation: Intensive Care Unit, Avicenne Teaching Hospital, 125 route de Stalingrad, 93000 Bobigny, France. christophe.clech@avc.aphp.fr
authors.forename: Christophe
authors.initials: C
authors.lastname: Clec'h
authors.forename: Corinne
authors.initials: C
authors.lastname: Alberti
authors.forename: François
authors.initials: F
authors.lastname: Vincent
authors.forename: Maïté
authors.initials: M
authors.lastname: Garrouste-Orgeas
authors.forename: Arnaud
authors.initials: A
authors.lastname: de Lassence
authors.forename: Dany
authors.initials: D
authors.lastname: Toledano
authors.forename: Elie
authors.initials: E
authors.lastname: Azoulay
authors.forename: Christophe
authors.initials: C
authors.lastname: Adrie
authors.forename: Samir
authors.initials: S
authors.lastname: Jamali
authors.forename: Isabelle
authors.initials: I
authors.lastname: Zaccaria
authors.forename: Yves
authors.initials: Y
authors.lastname: Cohen
authors.forename: Jean-François
authors.initials: JF
authors.lastname: Timsit
mesh_terms: D018806:APACHE; D000328:Adult; D000368:Aged; D002423:Cause of Death; D015986:Confounding Factors, Epidemiologic; D003422:Critical Care / Q000379:methods; D016638:Critical Illness / Q000401:mortality / Q000628:therapy; D005260:Female; D005602:France / Q000453:epidemiology; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D018579:Patient Selection; D011446:Prospective Studies; D012121:Respiration, Artificial; D012307:Risk Factors; D015983:Selection Bias; D016019:Survival Analysis; D013997:Time Factors; D014139:Tracheostomy / Q000009:adverse effects* / Q000401:mortality / Q000706:statistics & numerical data; D016896:Treatment Outcome