title: Comparison of the predictive validity of diagnosis-based risk adjusters for clinical outcomes.
abstract: OBJECTIVE
  Many possible methods of risk adjustment exist, but there is a dearth of comparative data on their performance. We compared the predictive validity of 2 widely used methods (Diagnostic Cost Groups [DCGs] and Adjusted Clinical Groups [ACGs]) for 2 clinical outcomes using a large national sample of patients.
  
  
  METHODS
  We studied all patients who used Veterans Health Administration (VA) medical services in fiscal year (FY) 2001 (n = 3,069,168) and assigned both a DCG and an ACG to each. We used logistic regression analyses to compare predictive ability for death or long-term care (LTC) hospitalization for age/gender models, DCG models, and ACG models. We also assessed the effect of adding age to the DCG and ACG models.
  
  
  RESULTS
  Patients in the highest DCG categories, indicating higher severity of illness, were more likely to die or to require LTC hospitalization. Surprisingly, the age/gender model predicted death slightly more accurately than the ACG model (c-statistic of 0.710 versus 0.700, respectively). The addition of age to the ACG model improved the c-statistic to 0.768. The highest c-statistic for prediction of death was obtained with a DCG/age model (0.830). The lowest c-statistics were obtained for age/gender models for LTC hospitalization (c-statistic 0.593). The c-statistic for use of ACGs to predict LTC hospitalization was 0.783, and improved to 0.792 with the addition of age. The c-statistics for use of DCGs and DCG/age to predict LTC hospitalization were 0.885 and 0.890, respectively, indicating the best prediction.
  
  
  CONCLUSIONS
  We found that risk adjusters based upon diagnoses predicted an increased likelihood of death or LTC hospitalization, exhibiting good predictive validity. In this comparative analysis using VA data, DCG models were generally superior to ACG models in predicting clinical outcomes, although ACG model performance was enhanced by the addition of age.
authors.affiliation: Houston Center for Quality of Care and Utilization Studies, a Health Services Research and Development Center of Excellence, Houston VA Medical Center, Houston, Texas 77030, USA.
authors.forename: Laura A
authors.initials: LA
authors.lastname: Petersen
authors.forename: Kenneth
authors.initials: K
authors.lastname: Pietz
authors.forename: LeChauncy D
authors.initials: LD
authors.lastname: Woodard
authors.forename: Margaret
authors.initials: M
authors.lastname: Byrne
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D005260:Female; D006787:Hospitals, Veterans / Q000706:statistics & numerical data*; D006801:Humans; D016015:Logistic Models; D008134:Long-Term Care; D008297:Male; D008875:Middle Aged; D009026:Mortality; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D020379:Risk Adjustment / Q000379:methods*; D014481:United States