title: Predicting mortality in patients with diabetes starting dialysis.
abstract: BACKGROUND
  While some prediction models have been developed for diabetic populations, prediction rules for mortality in diabetic dialysis patients are still lacking. Therefore, the objective of this study was to identify predictors for 1-year mortality in diabetic dialysis patients and use these results to develop a prediction model.
  
  
  METHODS
  Data were used from the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a multicenter, prospective cohort study in which incident patients with end stage renal disease (ESRD) were monitored until transplantation or death. For the present analysis, patients with DM at baseline were included. A prediction algorithm for 1-year all-cause mortality was developed through multivariate logistic regression. Candidate predictors were selected based on literature and clinical expertise. The final model was constructed through backward selection. The model's predictive performance, measured by calibration and discrimination, was assessed and internally validated through bootstrapping.
  
  
  RESULTS
  A total of 394 patients were available for statistical analysis; 82 (21%) patients died within one year after baseline (3 months after starting dialysis therapy). The final prediction model contained seven predictors; age, smoking, history of macrovascular complications, duration of diabetes mellitus, Karnofsky scale, serum albumin and hemoglobin level. Predictive performance was good, as shown by the c-statistic of 0.810. Internal validation showed a slightly lower, but still adequate performance. Sensitivity analyses showed stability of results.
  
  
  CONCLUSIONS
  A prediction model containing seven predictors has been identified in order to predict 1-year mortality for diabetic incident dialysis patients. Predictive performance of the model was good. Before implementing the model in clinical practice, for example for counseling patients regarding their prognosis, external validation is necessary.
authors.affiliation: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Merel
authors.initials: M
authors.lastname: van Diepen
authors.affiliation: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands; Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Marielle A
authors.initials: MA
authors.lastname: Schroijen
authors.affiliation: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands; Department of Endocrinology and Metabolic Diseases, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Olaf M
authors.initials: OM
authors.lastname: Dekkers
authors.affiliation: Department of Nephrology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Joris I
authors.initials: JI
authors.lastname: Rotmans
authors.affiliation: Department of Nephrology, Academic Medical Center, Amsterdam, the Netherlands.
authors.forename: Raymond T
authors.initials: RT
authors.lastname: Krediet
authors.affiliation: Hans Mak Institute, Naarden, the Netherlands.
authors.forename: Elisabeth W
authors.initials: EW
authors.lastname: Boeschoten
authors.affiliation: Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
authors.forename: Friedo W
authors.initials: FW
authors.lastname: Dekker
mesh_terms: D000368:Aged; D003920:Diabetes Mellitus / Q000401:mortality*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D012372:ROC Curve; D006435:Renal Dialysis / Q000401:mortality*; D015203:Reproducibility of Results