title: Predicting the duration of mechanical ventilation. The importance of disease and patient characteristics.
abstract: OBJECTIVE
  To analyze the determinants of an individual patient's duration of mechanical ventilation and assess interhospital variations for average durations of ventilation.
  
  
  METHODS
  Prospective, multicenter, inception, cohort study.
  
  
  METHODS
  Forty-two ICUs at 40 US hospitals.
  
  
  METHODS
  A total of 5,915 patients undergoing mechanical ventilation on ICU day 1 selected from the acute physiology and chronic health evaluation (APACHE) III database of 17,440 admissions.
  
  
  METHODS
  None.
  
  
  RESULTS
  Utilizing APACHE III data collected on the 5,915 patients, multivariate regression analysis was performed on selected patients and disease characteristics to determine which variables were significantly associated with the duration of mechanical ventilation. An equation predicting duration of ventilation was then developed using the significant predictor variables and its accuracy was evaluated. Variables significantly associated with duration of ventilation included primary reason for ICU admission, day 1 acute physiology score (APS) of APACHE III, age, prior patient location and hospital length of stay, activity limits due to respiratory disease, serum albumin, respiratory rate, and PaO2/FIo2 measurements. Using an equation derived from these variables, predicted durations of ventilation were then calculated and compared with actual observed durations for each of the 42 ICUs. Average duration of ventilation for the 42 ICUs ranged from 2.6 to 7.9 days, but 60% of this variation was accounted for by differences in patient characteristics.
  
  
  CONCLUSIONS
  For patients admitted to the ICU and ventilated on day 1, total duration of ventilation is primarily determined by admitting diagnosis and degree of physiologic derangement as measured by APS. An equation developed using multivariate regression techniques can accurately predict average duration of ventilation for groups of ICU patients, and we believe this equation will be useful for comparing ventilator practices between ICUs, controlling for patient differences in clinical trials of new therapies or weaning techniques, and as a quality improvement mechanism.
authors.affiliation: Department of Anesthesiology, George Washington University Medical Center, Washington, DC 20037, USA.
authors.forename: M G
authors.initials: MG
authors.lastname: Seneff
authors.forename: J E
authors.initials: JE
authors.lastname: Zimmerman
authors.forename: W A
authors.initials: WA
authors.lastname: Knaus
authors.forename: D P
authors.initials: DP
authors.lastname: Wagner
authors.forename: E A
authors.initials: EA
authors.lastname: Draper
mesh_terms: D018806:APACHE; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D007902:Length of Stay; D008875:Middle Aged; D015999:Multivariate Analysis; D011182:Postoperative Care; D011446:Prospective Studies; D012044:Regression Analysis; D012121:Respiration, Artificial; D013997:Time Factors