title: Impact of different measures of comorbid disease on predicted mortality of intensive care unit patients.
abstract: BACKGROUND
  Valid comparison of patient survival across ICUs requires adjustment for burden of chronic illness. The optimal measure of comorbidity in this setting remains uncertain.
  
  
  OBJECTIVE
  To examine the impact of different measures of comorbid disease on predicted mortality for ICU patients.
  
  
  METHODS
  Retrospective cohort study.
  
  
  METHODS
  Seventeen thousand eight hundred ninety-three veterans from 17 geographically diverse VA Medical Centers and 43 ICUs were studied, admitted between February 1, 1996 and July 31, 1997.
  
  
  METHODS
  ICD-9-CM codes reflecting comorbid disease from hospital stays before and including the index hospitalization from local VA computer databases were extracted, and three measures of comorbid disease were then compared: (1) an APACHE-weighted comorbidity score using comorbid diseases used in APACHE, (2) a count of conditions described by Elixhauser, and (3) Elixhauser comorbid diseases weighted independently. Univariate analyses and multivariate logistic regression models were used to determine the contribution of each measure to in-hospital mortality predictions.
  
  
  RESULTS
  Models using independently weighted Elixhauser comorbidities discriminated better than models using an APACHE-weighted score or a count of Elixhauser comorbidities. Twenty-three and 14 of the Elixhauser conditions were significant univariate and multivariable predictors of in-hospital mortality, respectively. In a multivariable model including all available predictors, comorbidity accounted for less (8.4%) of the model's uniquely attributable chi statistic than laboratory values (67.7%) and diagnosis (17.7%), but more than age (4.0%) and admission source (2.1%). Excluding codes from prior hospitalizations did not adversely affect model performance.
  
  
  CONCLUSIONS
  Independently weighted comorbid conditions identified through computerized discharge abstracts can contribute significantly to ICU risk adjustment models.
authors.affiliation: HSR&D, Veterans Affairs Medical Center, Cincinnati, Ohio, USA. JJOHNSTON2@indy.rr.com
authors.forename: Joseph A
authors.initials: JA
authors.lastname: Johnston
authors.forename: Douglas P
authors.initials: DP
authors.lastname: Wagner
authors.forename: Stephen
authors.initials: S
authors.lastname: Timmons
authors.forename: Deborah
authors.initials: D
authors.lastname: Welsh
authors.forename: Joel
authors.initials: J
authors.lastname: Tsevat
authors.forename: Marta L
authors.initials: ML
authors.lastname: Render
mesh_terms: D018806:APACHE; D002908:Chronic Disease / Q000145:classification / Q000453:epidemiology*; D015331:Cohort Studies; D015897:Comorbidity; D005260:Female; D017052:Hospital Mortality; D006787:Hospitals, Veterans / Q000706:statistics & numerical data*; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D038801:International Classification of Diseases; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D012189:Retrospective Studies; D020379:Risk Adjustment / Q000379:methods*; D014481:United States / Q000453:epidemiology; D014728:Veterans