title: A simple algorithm to predict incident kidney disease.
abstract: BACKGROUND
  Despite the growing burden of chronic kidney disease (CKD), there are no algorithms (to our knowledge) to quantify the effect of concurrent risk factors on the development of incident disease.
  
  
  METHODS
  A combined cohort (N = 14 155) of 2 community-based studies, the Atherosclerosis Risk in Communities Study and the Cardiovascular Health Study, was formed among men and women 45 years or older with an estimated glomerular filtration rate (GFR) exceeding 60 mL/min/1.73 m(2) at baseline. The primary outcome was the development of a GFR less than 60 mL/min/1.73 m(2) during a follow-up period of up to 9 years. Three prediction algorithms derived from the development data set were evaluated in the validation data set.
  
  
  RESULTS
  The 3 prediction algorithms were continuous and categorical best-fitting models with 10 predictors and a simplified categorical model with 8 predictors. All showed discrimination with area under the receiver operating characteristic curve in a range of 0.69 to 0.70. In the simplified model, age, anemia, female sex, hypertension, diabetes mellitus, peripheral vascular disease, and history of congestive heart failure or cardiovascular disease were associated with the development of a GFR less than 60 mL/min/1.73 m(2). A numeric score of at least 3 using the simplified algorithm captured approximately 70% of incident cases (sensitivity) and accurately predicted a 17% risk of developing CKD (positive predictive value).
  
  
  CONCLUSIONS
  An algorithm containing commonly understood variables helps to stratify middle-aged and older individuals at high risk for future CKD. The model can be used to guide population-level prevention efforts and to initiate discussions between practitioners and patients about risk for kidney disease.
authors.affiliation: Division of Nephrology and Hypertension, Department of Medicine, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7155, USA. sagar@med.unc.edu
authors.forename: Abhijit V
authors.initials: AV
authors.lastname: Kshirsagar
authors.forename: Heejung
authors.initials: H
authors.lastname: Bang
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Bomback
authors.forename: Suma
authors.initials: S
authors.lastname: Vupputuri
authors.forename: David A
authors.initials: DA
authors.lastname: Shoham
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Kern
authors.forename: Philip J
authors.initials: PJ
authors.lastname: Klemmer
authors.forename: Madhu
authors.initials: M
authors.lastname: Mazumdar
authors.forename: Phyllis A
authors.initials: PA
authors.lastname: August
mesh_terms: D000465:Algorithms; D002908:Chronic Disease; D005260:Female; D006801:Humans; D007674:Kidney Diseases / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D018570:Risk Assessment