title: [Use of the electroencephalographic spectral edge frequency 90 to monitor sedation of chronically ill patients].
abstract: BACKGROUND
  The depth of sedation required for patients in critical care units varies over time and should be subject to control. The clinical assessment scales used at present are inadequate, and several electroencephalographic variables have been investigated in recent years with the aim of quantifying depth of sedation. One such variable is the spectral edge frequency 90 (SEF90).
  
  
  OBJECTIVE
  To establish the correlation between SEF90 and the Ramsay score as indicators of depth of sedation. To estimate the ability of SEF90 to predict sedation and a patient's hemodynamic response during aspiration of secretions through the orotracheal tube.
  
  
  METHODS
  Patients in a surgical intensive care unit. The ability of SEF90 to predict a certain Ramsay score was assessed by logistic regression. We also calculated the predictive probability (Pk) of SEF90 for the appearance of hemodynamic change and of movement in the event of endotracheal aspiration.
  
  
  RESULTS
  When SEF90 was < 16 Hz, the probability of a Ramsay score >= 4 was >= 90% (Pk = 0.91). Neither SEF90 nor the Ramsay score predicted hemodynamic response to orotracheal aspiration.
  
  
  CONCLUSIONS
  SEF90 distinguishes superficial from deep sedation but does not differentiate further degrees of depth or the likelihood of hemodynamic instability or movement in response to aspiration.
authors.affiliation: Servicio de Anestesiología y Reanimación. UCI Quirúrgica. Unitat de Recursos i Serveis Comuns. Hospital Clínic Barcelona, Spain. febregas@medicina.ub.es
authors.forename: N
authors.initials: N
authors.lastname: Fàbregas
authors.forename: P
authors.initials: P
authors.lastname: Gambús
authors.forename: I
authors.initials: I
authors.lastname: Trocóniz
authors.forename: R
authors.initials: R
authors.lastname: Valero
authors.forename: E
authors.initials: E
authors.lastname: Guix
authors.forename: E
authors.initials: E
authors.lastname: Zavala
mesh_terms: D000758:Anesthesia / Q000145:classification*; D016638:Critical Illness; D016002:Discriminant Analysis; D004569:Electroencephalography; D005260:Female; D006439:Hemodynamics; D006801:Humans; D008297:Male; D008875:Middle Aged; D009068:Movement; D012044:Regression Analysis