title: Intensive care unit length of stay: Benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV.
abstract: OBJECTIVE
  To revise and update the Acute Physiology and Chronic Health Evaluation (APACHE) model for predicting intensive care unit (ICU) length of stay.
  
  
  METHODS
  Observational cohort study.
  
  
  METHODS
  One hundred and four ICUs in 45 U.S. hospitals.
  
  
  METHODS
  Patients included 131,618 consecutive ICU admissions during 2002 and 2003, of which 116,209 met inclusion criteria.
  
  
  METHODS
  None.
  
  
  RESULTS
  The APACHE IV model for predicting ICU length of stay was developed using ICU day 1 patient data and a multivariate linear regression procedure to estimate the precise ICU stay for randomly selected patients who comprised 60% of the database. New variables were added to the previous APACHE III model, and advanced statistical modeling techniques were used. Accuracy was assessed by comparing mean observed and mean predicted ICU stay for the excluded 40% of patients. Aggregate mean observed ICU stay was 3.86 days and mean predicted 3.78 days (p < .001), a difference of 1.9 hrs. For 108 (93%) of 116 diagnoses, there was no significant difference between mean observed and mean predicted ICU stay. The model accounted for 21.5% of the variation in ICU stay across individual patients and 62% across ICUs. Correspondence between mean observed and mean predicted length of stay was reduced for patients with a short (< or =1.7 days) or long (> or =9.4 days) ICU stay and a low (<20%) or high (>80%) risk of death on ICU day 1.
  
  
  CONCLUSIONS
  The APACHE IV model provides clinically useful ICU length of stay predictions for critically ill patient groups, but its accuracy and utility are limited for individual patients. APACHE IV benchmarks for ICU stay are useful for assessing the efficiency of unit throughput and support examination of structural, managerial, and patient factors that affect ICU stay.
authors.affiliation: George Washington University, Washington, DC, USA.
authors.forename: Jack E
authors.initials: JE
authors.lastname: Zimmerman
authors.forename: Andrew A
authors.initials: AA
authors.lastname: Kramer
authors.forename: Douglas S
authors.initials: DS
authors.lastname: McNair
authors.forename: Fern M
authors.initials: FM
authors.lastname: Malila
authors.forename: Violet L
authors.initials: VL
authors.lastname: Shaffer
mesh_terms: D018806:APACHE; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019985:Benchmarking / Q000379:methods*; D002138:Calibration; D015331:Cohort Studies; D016638:Critical Illness / Q000145:classification*; D006295:Health Resources / Q000706:statistics & numerical data; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data*; D016014:Linear Models; D008875:Middle Aged; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care / Q000379:methods; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D014481:United States