title: A clinical stratification tool for chronic kidney disease progression rate based on classification tree analysis.
abstract: BACKGROUND
  Registry-based studies have identified risk factors for chronic kidney disease (CKD) and for progression to end-stage renal disease. However, usually, these studies do not incorporate sequential measurements of kidney function and provide little information on the prognosis of individual patients. The aim of this study is to identify which combinations of demographic and clinical characteristics are useful to discriminate patients with a differential annual decline in glomerular filtration rate (GFR).
  
  
  METHODS
  This observational retrospective study includes patients enlisted in the registry of the Prevention of Progressive Renal Insufficiency Project of Emilia-Romagna region (Italy) from July 2004 to June 2010, with at least four serum creatinine measurements. Classification tree analysis (CTA) was used to identify subgroups of patients with a different annual GFR decline using demographic and laboratory data collected at study entry.
  
  
  RESULTS
  The CTA procedure generated seven mutually exclusive groups. Among patients with proteinuria, those with a baseline estimated GFR (eGFR) of >33 mL/min/1.73 m(2) exhibited the fastest illness progression in the study population (-3.655 mL/min/1.73 m(2)), followed by patients with a baseline eGFR of <33 mL/min/1.73 m(2) and a baseline serum phosphorus of >4.3 mg/dL (-2.833 mL/min/1.73 m(2)). Among patients without proteinuria, those aged <67 years exhibited a significantly faster progression, which was even faster for the subgroup with diabetes. Among patients aged >67 years, females had on average a stable eGFR over time, with a large variability.
  
  
  CONCLUSIONS
  It is possible to rely on a few variables typically accessible in routine clinical practice to stratify patients with a different CKD progression rate. Stratification can be used to guide decisions about the follow-up schedule, treatments to slow progression of kidney disease, prevent its complications and to begin planning for dialysis and transplantation.
authors.affiliation: Department of Biomedical and Neuromotor Sciences, Alma Mater Studiorum - University of Bologna, Bologna, Italy.
authors.forename: Paola
authors.initials: P
authors.lastname: Rucci
authors.forename: Marcora
authors.initials: M
authors.lastname: Mandreoli
authors.forename: Dino
authors.initials: D
authors.lastname: Gibertoni
authors.forename: Alessandro
authors.initials: A
authors.lastname: Zuccalà
authors.forename: Maria Pia
authors.initials: MP
authors.lastname: Fantini
authors.forename: Jacopo
authors.initials: J
authors.lastname: Lenzi
authors.forename: Antonio
authors.initials: A
authors.lastname: Santoro
mesh_terms: D000368:Aged; D000704:Analysis of Variance; D018450:Disease Progression; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007677:Kidney Function Tests; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D051436:Renal Insufficiency, Chronic / Q000145:classification / Q000473:pathology* / Q000503:physiopathology; D012189:Retrospective Studies; D012307:Risk Factors; D012720:Severity of Illness Index
keywords: CKD progression; decision tree; prediction models