title: Using the Johns Hopkins Aggregated Diagnosis Groups (ADGs) to predict mortality in a general adult population cohort in Ontario, Canada.
abstract: BACKGROUND
  Administrative healthcare databases are increasingly used for health services and comparative effectiveness research. When comparing outcomes between different treatments, interventions, or exposures, the ability to adjust for differences in the risk of the outcome occurring between treatment groups is important. Similarly, when conducting healthcare provider profiling, adequate risk-adjustment is necessary for conclusions about provider performance to be valid. There are limited validated methods for risk adjustment in ambulatory populations using administrative healthcare databases.
  
  
  OBJECTIVE
  To examine the ability of the Johns Hopkins' Aggregated Diagnosis Groups (ADGs) to predict mortality in a general ambulatory population cohort.
  
  
  METHODS
  Retrospective cohort constructed using population-based administrative data.
  
  
  METHODS
  All 10,498,413 residents of Ontario, Canada between the ages of 20 and 100 years who were alive on their birthday in 2007. Subjects were randomly divided into derivation and validation samples.
  
  
  METHODS
  Death within 1 year of the subject's birthday in 2007.
  
  
  RESULTS
  A logistic regression model consisting of age, sex, and indicator variables for 28 of the 32 ADG categories had excellent discrimination: the c-statistic (equivalent to the area under the receiver operating characteristic curve) was 0.917 in both derivation and validation samples. Furthermore, the model showed very good calibration. In comparison, the use of the Charlson comorbidity index or the Elixhauser comorbidities resulted in a minor decrease in discrimination compared with the use of the ADGs.
  
  
  CONCLUSIONS
  Logistic regression models using age, sex, and the John Hopkins ADGs were able to accurately predict 1-year mortality in a general ambulatory population of subjects.
authors.affiliation: Institute for Clinical Evaluative Sciences, Department of Health Management, Policy and Evaluation, University of Ottawa, Ottawa, Ontario, Canada. peter.austin@ices.on.ca
authors.forename: Peter C
authors.initials: PC
authors.lastname: Austin
authors.forename: Carl
authors.initials: C
authors.lastname: van Walraven
authors.forename: Walter P
authors.initials: WP
authors.lastname: Wodchis
authors.forename: Alice
authors.initials: A
authors.lastname: Newman
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Anderson
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015897:Comorbidity; D016208:Databases, Factual; D003953:Diagnosis-Related Groups; D005260:Female; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009026:Mortality / Q000639:trends*; D009864:Ontario / Q000453:epidemiology; D011237:Predictive Value of Tests; D012372:ROC Curve; D012189:Retrospective Studies; D020379:Risk Adjustment