title: A predictive model of chronic kidney disease in patients with congenital anomalies of the kidney and urinary tract.
abstract: BACKGROUND
  The antenatal detection of congenital anomalies of the kidney and urinary tract (CAKUT) has permitted early management of these conditions. The aim of this study was to identify predictive factors associated with chronic kidney disease (CKD) in CAKUT. We also propose a risk score of CKD.
  
  
  METHODS
  In this cohort study, 822 patients with prenatally detected CAKUT were followed up for a median time of 43 months. The primary outcome was CKD stage III or higher. A predictive model was developed using the Cox proportional hazards model and evaluated by using c statistics.
  
  
  RESULTS
  Chronic kidney disease occurred in 49 of the 822 (6 %) children with prenatally detected CAKUT. The most accurate model included bilateral hydronephrosis, oligohydramnios, estimated glomerular filtration rate and postnatal diagnosis. The accuracy of the score was 0.95 [95 % confidence interval (CI) 0.89-0.99] and 0.92 (95 % CI 0.86-0.95) after a follow-up of 2 and 10 years, respectively. Based on survival curves, we estimated that at 10 years of age, the probability of survival without CKD stage III was approximately 98 and 58 % for the patients assigned to the low-risk and high-risk groups, respectively (p < 0.001).
  
  
  CONCLUSIONS
  Our predictive model of CKD may contribute to an early identification of a subgroup of patients at high risk for renal impairment. It should be pointed out, however, that this model requires external validation in a different cohort.
authors.affiliation: Pediatric Nephrology Unit, Department of Pediatrics, National Institute of Science and Technology (INCT) of Molecular Medicine, Faculty of Medicine, Federal University of Minas Gerais, Av Alfredo Balena, 190, Belo Horizonte, MG, 30130-100, Brazil.
authors.forename: Isabel G
authors.initials: IG
authors.lastname: Quirino
authors.forename: Cristiane S
authors.initials: CS
authors.lastname: Dias
authors.forename: Mariana A
authors.initials: MA
authors.lastname: Vasconcelos
authors.forename: Isabel V
authors.initials: IV
authors.lastname: Poggiali
authors.forename: Kerlane C
authors.initials: KC
authors.lastname: Gouvea
authors.forename: Alamanda K
authors.initials: AK
authors.lastname: Pereira
authors.forename: Gabriela P
authors.initials: GP
authors.lastname: Paulinelli
authors.forename: Amanda R
authors.initials: AR
authors.lastname: Moura
authors.forename: Raquel S
authors.initials: RS
authors.lastname: Ferreira
authors.forename: Enrico A
authors.initials: EA
authors.lastname: Colosimo
authors.forename: Ana Cristina
authors.initials: AC
authors.lastname: Simões E Silva
authors.forename: Eduardo A
authors.initials: EA
authors.lastname: Oliveira
mesh_terms: D019540:Area Under Curve; D015331:Cohort Studies; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D007668:Kidney / Q000002:abnormalities*; D008297:Male; D016016:Proportional Hazards Models; D012372:ROC Curve; D051436:Renal Insufficiency, Chronic / Q000453:epidemiology*; D012307:Risk Factors; D014551:Urinary Tract / Q000002:abnormalities*