title: A predictive model for progression of chronic kidney disease to kidney failure.
abstract: BACKGROUND
  Chronic kidney disease (CKD) is common. Kidney disease severity can be classified by estimated glomerular filtration rate (GFR) and albuminuria, but more accurate information regarding risk for progression to kidney failure is required for clinical decisions about testing, treatment, and referral.
  
  
  OBJECTIVE
  To develop and validate predictive models for progression of CKD.
  
  
  METHODS
  Development and validation of prediction models using demographic, clinical, and laboratory data from 2 independent Canadian cohorts of patients with CKD stages 3 to 5 (estimated GFR, 10-59 mL/min/1.73 m(2)) who were referred to nephrologists between April 1, 2001, and December 31, 2008. Models were developed using Cox proportional hazards regression methods and evaluated using C statistics and integrated discrimination improvement for discrimination, calibration plots and Akaike Information Criterion for goodness of fit, and net reclassification improvement (NRI) at 1, 3, and 5 years.
  
  
  METHODS
  Kidney failure, defined as need for dialysis or preemptive kidney transplantation.
  
  
  RESULTS
  The development and validation cohorts included 3449 patients (386 with kidney failure [11%]) and 4942 patients (1177 with kidney failure [24%]), respectively. The most accurate model included age, sex, estimated GFR, albuminuria, serum calcium, serum phosphate, serum bicarbonate, and serum albumin (C statistic, 0.917; 95% confidence interval [CI], 0.901-0.933 in the development cohort and 0.841; 95% CI, 0.825-0.857 in the validation cohort). In the validation cohort, this model was more accurate than a simpler model that included age, sex, estimated GFR, and albuminuria (integrated discrimination improvement, 3.2%; 95% CI, 2.4%-4.2%; calibration [Nam and D'Agostino χ(2) statistic, 19 vs 32]; and reclassification for CKD stage 3 [NRI, 8.0%; 95% CI, 2.1%-13.9%] and for CKD stage 4 [NRI, 4.1%; 95% CI, -0.5% to 8.8%]).
  
  
  CONCLUSIONS
  A model using routinely obtained laboratory tests can accurately predict progression to kidney failure in patients with CKD stages 3 to 5.
authors.affiliation: Department of Medicine, Tufts Medical Center, 800 Washington St, PO Box 391, Boston, MA 02111, USA. ntangri@tuftsmedicalcenter.org
authors.forename: Navdeep
authors.initials: N
authors.lastname: Tangri
authors.forename: Lesley A
authors.initials: LA
authors.lastname: Stevens
authors.forename: John
authors.initials: J
authors.lastname: Griffith
authors.forename: Hocine
authors.initials: H
authors.lastname: Tighiouart
authors.forename: Ognjenka
authors.initials: O
authors.lastname: Djurdjev
authors.forename: David
authors.initials: D
authors.lastname: Naimark
authors.forename: Adeera
authors.initials: A
authors.lastname: Levin
authors.forename: Andrew S
authors.initials: AS
authors.lastname: Levey
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000419:Albuminuria; D015415:Biomarkers / Q000032:analysis*; D002118:Calcium / Q000097:blood; D002138:Calibration; D015331:Cohort Studies; D018450:Disease Progression; D005260:Female; D005544:Forecasting; D005919:Glomerular Filtration Rate; D006801:Humans; D007674:Kidney Diseases / Q000097:blood / Q000145:classification* / Q000150:complications* / Q000652:urine; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D051437:Renal Insufficiency / Q000209:etiology*; D012306:Risk; D018570:Risk Assessment; D012709:Serum Albumin; D012737:Sex Factors
chemical_list: D015415:Biomarkers; D012709:Serum Albumin; D002118:Calcium