title: Chronic nephropathies: individual risk for progression to end-stage renal failure as predicted by an integrated probabilistic model.
abstract: OBJECTIVE
  To predict risk of end-stage renal disease (ESRD) in individual patients with chronic nephropathy.
  
  
  METHODS
  Sequential use of univariate analyses and Cox regression to identify risk factors, artificial neural network to quantify their relative importance and Bayesian analysis to address uncertainty of relationships and incorporate ESRD prevalence information in 344 patients with chronic nephropathy enrolled in the Ramipril Efficacy In Nephropathy study.
  
  
  RESULTS
  Serum creatinine (SC), 24-hour urinary protein excretion (UPE) and calcium-phosphorus (Ca*P) product were, in this order, the strongest time-adjusted ESRD predictors. Individual risk of ESRD ranged from near zero when SC and UPE were <1.66 mg/dl and <3 g/24 h, to 69% when SC, UPE and Ca*P were > or =2.41 mg/dl, > or =3 g/24 h and > or =32.64 mg2/dl2, respectively. Receiver operating characteristic curves showed that within lowest, middle and highest tertiles of basal SC (0.90-1.65, 1.66-2.40 and 2.41-6.30 mg/dl, respectively) the model accurately predicted ESRD (AUC = 0.80, 0.72 and 0.65; p = 0.0003, 0.0001 and 0.0022, respectively), quality of life or treatment costs.
  
  
  CONCLUSIONS
  Integrated use of regression analysis and probabilistic models allows computation of individual risk of progression to ESRD and related utilities. This may help in optimizing care and costs in nephrology and other medical areas and designing trials in high-risk patients.
authors: Clinical Research Center for Rare Diseases Aldo e Cele Daccò, Mario Negri Institute for Pharmacological Research, Villa Camozzi, Ranica, Bergamo, Italy.
authors: Borislav D
authors: BD
authors: Dimitrov
authors: Piero
authors: P
authors: Ruggenenti
authors: Rumen
authors: R
authors: Stefanov
authors: Annalisa
authors: A
authors: Perna
authors: Giuseppe
authors: G
authors: Remuzzi
mesh_terms: D000704:Analysis of Variance; D001499:Bayes Theorem; D015415:Biomarkers / Q000097:blood / Q000652:urine; D002908:Chronic Disease; D003404:Creatinine / Q000097:blood; D003663:Decision Trees; D018450:Disease Progression; D006801:Humans; D007674:Kidney Diseases / Q000097:blood / Q000150:complications; D007676:Kidney Failure, Chronic / Q000191:economics / Q000209:etiology*; D015233:Models, Statistical; D016571:Neural Networks, Computer; D016016:Proportional Hazards Models; D011507:Proteinuria / Q000652:urine; D006435:Renal Dialysis / Q000191:economics / Q000706:statistics & numerical data; D012307:Risk Factors
chemical_list: D015415:Biomarkers; D003404:Creatinine