title: The use of risk predictions to identify candidates for intermediate care units. Implications for intensive care utilization and cost.
abstract: OBJECTIVE
  To develop a predictive equation that estimates the probability of life-supporting therapy among ICU monitor admissions and to explore its potential for reducing cost and improving ICU utilization.
  
  
  METHODS
  Prospective inception cohort analysis.
  
  
  METHODS
  Forty-two ICUs in 40 US hospitals with more than 200 beds and a consecutive sample of 17,440 ICU admissions.
  
  
  METHODS
  A multivariate equation was developed to estimate the probability of life support for ICU monitoring admissions during an entire ICU stay.
  
  
  METHODS
  Demographic, physiologic, and treatment information obtained during the first 24 h in the ICU and over the first 7 ICU days.
  
  
  RESULTS
  The most important determinants of subsequent risk for life-supporting (active) treatment were diagnosis, the acute physiology score of APACHE III, age, operative status, and the patient's location and hospital length of stay before ICU admission. Among 8,040 ICU monitoring admissions, 6,180 (76.8%) had a low (< 10%) risk for receiving active treatment during the ICU stay; 95.6% received no subsequent active treatment. Review of outcomes and the type and amount of therapy received suggest that most low-risk ICU monitor admissions could be safely cared for in an intermediate care setting.
  
  
  CONCLUSIONS
  Objective predictions can accurately identify groups of ICU admissions who are at a low risk for receiving life support. This capability can be used to assess ICU resource use and develop strategies for providing graded critical care services at a reduced cost.
authors.affiliation: Department of Anesthesiology, George Washington University Medical Center, Washington, DC 20037, USA.
authors.forename: J E
authors.initials: JE
authors.lastname: Zimmerman
authors.forename: D P
authors.initials: DP
authors.lastname: Wagner
authors.forename: W A
authors.initials: WA
authors.lastname: Knaus
authors.forename: J F
authors.initials: JF
authors.lastname: Williams
authors.forename: D
authors.initials: D
authors.lastname: Kolakowski
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; D015331:Cohort Studies; D017046:Cost Savings / Q000191:economics / Q000706:statistics & numerical data; D005260:Female; D017721:Hospital Costs / Q000706:statistics & numerical data; D006757:Hospital Units / Q000191:economics / Q000706:statistics & numerical data*; D006801:Humans; D007362:Intensive Care Units / Q000191:economics / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D016017:Odds Ratio; D011379:Prognosis; D011383:Progressive Patient Care / Q000191:economics / Q000706:statistics & numerical data*; D011446:Prospective Studies; D018570:Risk Assessment; D014481:United States