title: Prognostic model for total mortality in patients with haemodialysis from the Assessments of Survival and Cardiovascular Events (AURORA) study.
abstract: OBJECTIVE
  Risk factors of mortality in patients with haemodialysis (HD) have been identified in several studies, but few prognostic models have been developed with assessments of calibration and discrimination abilities. We used the database of the Assessment of Survival and Cardiovascular Events study to develop a prognostic model of mortality over 3-4 years.
  
  
  METHODS
  Five factors (age, albumin, C-reactive protein, history of cardiovascular disease and diabetes) were selected from experience and forced into the regression equation. In a 67% random try-out sample of patients, no further factors amongst 24 candidates added significance (P < 0.01) to mortality outcome as assessed by Cox regression modelling, and individual probabilities of death were estimated in the try-out and test samples. Calibration was explored by calculating the prognostic index with regression coefficients from the try-out sample to patients in the 33% test sample. Discrimination was assessed by receiver operating characteristic (ROC) areas.
  
  
  RESULTS
  The strongest prognostic factor in the try-out sample was age, with small differences between the other four factors. Calibration in the test sample was good when the calculated number of deaths was multiplied by a constant of 1.33. The five-factor model discriminated reasonably well between deceased and surviving patients in both the try-out and test samples with an ROC area of about 0.73.
  
  
  CONCLUSIONS
  A model consisting of five factors can be used to estimate and stratify the probability of death for individuals The model is most useful for long-term prognosis in an HD population with survival prospects of more than 1 year.
authors.affiliation: Oslo University Hospital, Ullevål, Oslo, Norway. ingar.holme@uus.no
authors.forename: I
authors.initials: I
authors.lastname: Holme
authors.forename: B C
authors.initials: BC
authors.lastname: Fellström
authors.forename: A G
authors.initials: AG
authors.lastname: Jardin
authors.forename: R E
authors.initials: RE
authors.lastname: Schmieder
authors.forename: F
authors.initials: F
authors.lastname: Zannad
authors.forename: H
authors.initials: H
authors.lastname: Holdaas
mesh_terms: D000367:Age Factors; D000368:Aged; D002097:C-Reactive Protein / Q000032:analysis; D002318:Cardiovascular Diseases / Q000453:epidemiology*; D015897:Comorbidity; D003920:Diabetes Mellitus / Q000453:epidemiology*; D005260:Female; D006436:Hemodialysis Units, Hospital / Q000706:statistics & numerical data; D006801:Humans; D007676:Kidney Failure, Chronic / Q000097:blood / Q000453:epidemiology* / Q000628:therapy; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D006435:Renal Dialysis / Q000401:mortality / Q000706:statistics & numerical data; D012307:Risk Factors; D012709:Serum Albumin / Q000032:analysis
chemical_list: D012709:Serum Albumin; D002097:C-Reactive Protein