title: Predicting the risk of 1-year mortality in incident dialysis patients: accounting for case-mix severity in studies using administrative data.
abstract: BACKGROUND
  Administrative databases are increasingly being used to study the incident dialysis population and have important advantages. However, traditional methods of risk adjustment have limitations in this patient population.
  
  
  OBJECTIVE
  Our objective was to develop a prognostic index for 1-year mortality in incident dialysis patients using administrative data that was applicable to ambulatory patients, used objective definitions of candidate predictor variables, and was easily replicated in other environments.
  
  
  METHODS
  Anonymized, administrative health data housed at the Institute for Clinical Evaluative Sciences in Toronto, Canada were used to identify a population-based sample of 16,205 patients who initiated dialysis between July 1, 1998 and March 31, 2005. The cohort was divided into derivation, validation, and testing samples and 4 different strategies were used to derive candidate logistic regression models for 1-year mortality. The final risk prediction model was selected based on discriminatory ability (as measured by the c-statistic) and a risk prediction score was derived using methods adopted from the Framingham Heart Study. Calibration of the predictive model was assessed graphically.
  
  
  RESULTS
  The risk of death during the first year of dialysis therapy was 16.4% in the derivation sample. The final model had a c-statistic of 0.765, 0.763, and 0.756 in the derivation, validation, and testing samples, respectively. Plots of actual versus predicted risk of death at 1-year showed good calibration.
  
  
  CONCLUSIONS
  The prognostic index and summary risk score accurately predict 1-year mortality in incident dialysis patients and can be used for the purposes of risk adjustment.
authors.affiliation: Faculty of Medicine, University of Calgary, Calgary, Alberta, Canada. rrquinn@gmail.com
authors.forename: Robert R
authors.initials: RR
authors.lastname: Quinn
authors.forename: Andreas
authors.initials: A
authors.lastname: Laupacis
authors.forename: Janet E
authors.initials: JE
authors.lastname: Hux
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Oliver
authors.forename: Peter C
authors.initials: PC
authors.lastname: Austin
mesh_terms: D015331:Cohort Studies; D003953:Diagnosis-Related Groups; D005260:Female; D006801:Humans; D007676:Kidney Failure, Chronic / Q000401:mortality / Q000628:therapy; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009864:Ontario / Q000453:epidemiology; D006435:Renal Dialysis / Q000401:mortality* / Q000706:statistics & numerical data; D020379:Risk Adjustment; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors; D016896:Treatment Outcome