title: Automated intensive care unit risk adjustment: results from a National Veterans Affairs study.
abstract: BACKGROUND
  Comparison of outcome among intensive care units (ICUs) requires risk adjustment for differences in severity of illness and risk of death at admission to the ICU, historically obtained by costly chart review and manual data entry.
  
  
  OBJECTIVE
  To accurately estimate patient risk of death in the ICU using data easily available in hospital electronic databases to permit automation.
  
  
  METHODS
  Cohort study to develop and validate a model to predict mortality at hospital discharge using multivariate logistic regression with a split derivation (17,731) and validation (11,646) sample formed from 29,377 consecutive first ICU admissions to medical, cardiac, and surgical ICUs in 17 Veterans' Health Administration hospitals between February 1996 and July 1997.
  
  
  METHODS
  Mortality at hospital discharge adjusted for age, laboratory data, diagnosis, source of ICU admission, and comorbid illness.
  
  
  RESULTS
  The overall hospital death rate was 11.3%. In the validation sample, the model separated well between survivors and nonsurvivors (area under the receiver operating characteristic curve = 0.885). Examination of the observed vs. the predicted mortality across the range of mortality showed the model was well calibrated.
  
  
  CONCLUSIONS
  Automation could broaden access to risk adjustment of ICU outcomes with only a small trade-off in discrimination. Broader use might promote valid evaluation of ICU outcomes, encouraging effective practices and improving ICU quality.
authors.affiliation: Veterans' Affairs Medical Center-Cincinnati, 3200 Vine Street (111F), Cincinnati, OH 45220-2288, USA. marta.render@med.va.gov
authors.forename: Marta L
authors.initials: ML
authors.lastname: Render
authors.forename: H Myra
authors.initials: HM
authors.lastname: Kim
authors.forename: Deborah E
authors.initials: DE
authors.lastname: Welsh
authors.forename: Stephen
authors.initials: S
authors.lastname: Timmons
authors.forename: Joseph
authors.initials: J
authors.lastname: Johnston
authors.forename: Siu
authors.initials: S
authors.lastname: Hui
authors.forename: Alfred F
authors.initials: AF
authors.lastname: Connors
authors.forename: Douglas
authors.initials: D
authors.lastname: Wagner
authors.forename: Jennifer
authors.initials: J
authors.lastname: Daley
authors.forename: Timothy P
authors.initials: TP
authors.lastname: Hofer
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001331:Automation; D002138:Calibration; D005260:Female; D006751:Hospital Information Systems / Q000706:statistics & numerical data*; D017052:Hospital Mortality; D006787:Hospitals, Veterans / Q000592:standards* / Q000706:statistics & numerical data; D006801:Humans; D007362:Intensive Care Units / Q000592:standards* / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D015203:Reproducibility of Results; D020379:Risk Adjustment / Q000379:methods*; D018570:Risk Assessment; D012720:Severity of Illness Index; D014481:United States / Q000453:epidemiology