title: Prediction of renal insufficiency in Pima Indians with nephropathy of type 2 diabetes mellitus.
abstract: BACKGROUND
  A high prevalence and early onset of type 2 diabetes in Pima Indians is well known. Our objective is to use several statistical models to identify predictors of glomerular filtration rate (GFR) deterioration and develop an algorithm to predict GFR 4 years after the initial evaluation.
  
  
  METHODS
  All records (n = 86) were randomly assigned to a training set (n = 60) and a testing set (n = 26). Linear regression, generalized additive, tree-based, and artificial neural network models were used to identify predictors of outcome and develop a prediction algorithm.
  
  
  RESULTS
  Proteinuria remained the single most important predictor of long-term renal function; other predictors included baseline GFR, blood pressure, plasma renin activity, lipid profile, age, weight/body mass index, and diabetes duration. All four models achieved a good correlation (r = 0.73 to 0.78) between observed and predicted 4-year GFRs on a separate (testing) data set. Best results in predicting the value of GFR were achieved using a tree-based model with six terminal nodes (r = 0.78; root mean squared prediction error = 38.9). The tree-based and generalized additive models achieved high positive (91%) and negative (100%) predictive values in identifying subjects, who developed depressed GFRs in 4 years. An artificial neural network achieved the highest area under the receiver operating characteristic curve (0.91).
  
  
  CONCLUSIONS
  GFR depression within 4 years can be predicted with a precision that suggests potential clinical utility. A tree-based model with six terminal nodes has shown the best results in predicting the actual value of GFR, whereas an artificial neural network is the model of choice to identify the group of patients that will develop renal insufficiency.
authors.affiliation: Division of Nephrology and Hypertension, University of Utah School of Medicine, Salt Lake City, UT 84112, USA. alex.goldfarb@hsc.utah.edu
authors.forename: Alexander S
authors.initials: AS
authors.lastname: Goldfarb-Rumyantzev
authors.forename: Lisa
authors.initials: L
authors.lastname: Pappas
mesh_terms: D000328:Adult; D000367:Age Factors; D001794:Blood Pressure / Q000502:physiology; D015992:Body Mass Index; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000453:epidemiology / Q000503:physiopathology; D003928:Diabetic Nephropathies / Q000453:epidemiology* / Q000503:physiopathology; D005260:Female; D005919:Glomerular Filtration Rate / Q000502:physiology; D006801:Humans; D007198:Indians, North American / Q000706:statistics & numerical data*; D007677:Kidney Function Tests / Q000379:methods / Q000706:statistics & numerical data; D008297:Male; D015233:Models, Statistical; D011237:Predictive Value of Tests; D011507:Proteinuria / Q000175:diagnosis; D051437:Renal Insufficiency / Q000175:diagnosis* / Q000453:epidemiology / Q000503:physiopathology; D012083:Renin / Q000097:blood; D012307:Risk Factors
chemical_list: D012083:Renin