title: Variation in outcomes in Veterans Affairs intensive care units with a computerized severity measure.
abstract: OBJECTIVE
  To quantify the variability in risk-adjusted mortality and length of stay of Veterans Affairs intensive care units using a computer-based severity of illness measure.
  
  
  METHODS
  Retrospective cohort study.
  
  
  METHODS
  A stratified random sample of 34 intensive care units in 17 Veterans Affairs hospitals.
  
  
  METHODS
  A consecutive sample of 29,377 first intensive care unit admissions from February 1996 through July 1997.
  
  
  METHODS
  Standardized mortality ratio (observed/expected deaths) and observed minus expected length of stay (OMELOS) with 95% confidence intervals were estimated for each unit using a hierarchical logistic (standardized mortality ratio) or linear (OMELOS) regression model with Markov Chain Monte Carlo simulation. We adjusted for patient characteristics including age, admission diagnosis, comorbid disease, physiology at admission (from laboratory data), and transfer status.
  
  
  RESULTS
  Mortality across the intensive care units for the 12,088 surgical and 17,289 medical cases averaged 11% (range, 2-30%). Length of stay in the intensive care units averaged 4.0 days (range, mean unit length of stay 3.0-5.9). Standardized mortality ratio of the intensive care units varied from 0.62 to 1.27; the standardized mortality ratio and 95% confidence interval were <1 for four intensive care units and >1.0 for seven intensive care units. OMELOS of the intensive care units ranged from -0.89 to 1.34 days. In a random slope hierarchical model, variation in standardized mortality ratio among intensive care units was similar across the range of severity, whereas variation in length of stay increased with severity. Standardized mortality ratio was not associated with OMELOS (Pearson's r = .13).
  
  
  CONCLUSIONS
  We identified intensive care units whose indicators for mortality and length of stay differ substantially using a conservative statistical approach with a severity adjustment model based on data available in computerized clinical databases. Computerized risk adjustment employing routinely available data may facilitate research on the utility of intensive care unit profiling and analysis of natural experiments to understand process and outcome links and quality efforts.
authors.affiliation: VAMC-Cincinnati, Ohio 45220, USA.
authors.forename: Marta L
authors.initials: ML
authors.lastname: Render
authors.forename: H Myra
authors.initials: HM
authors.lastname: Kim
authors.forename: James
authors.initials: J
authors.lastname: Deddens
authors.forename: Siva
authors.initials: S
authors.lastname: Sivaganesin
authors.forename: Deborah E
authors.initials: DE
authors.lastname: Welsh
authors.forename: Karen
authors.initials: K
authors.lastname: Bickel
authors.forename: Ron
authors.initials: R
authors.lastname: Freyberg
authors.forename: Stephen
authors.initials: S
authors.lastname: Timmons
authors.forename: Joseph
authors.initials: J
authors.lastname: Johnston
authors.forename: Alfred F
authors.initials: AF
authors.lastname: Connors
authors.forename: Douglas
authors.initials: D
authors.lastname: Wagner
authors.forename: Timothy P
authors.initials: TP
authors.lastname: Hofer
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D003201:Computers; D016001:Confidence Intervals; D005260:Female; D017052:Hospital Mortality; D006787:Hospitals, Veterans; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay; D008297:Male; D008490:Medical Informatics / Q000706:statistics & numerical data*; D008875:Middle Aged; D011379:Prognosis; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000379:methods*; D012720:Severity of Illness Index; D014481:United States