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      Nonlinear trajectory of GFR in children before RRT.

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          Abstract

          GFR decline in patients with CKD has been widely approximated using linear models, but this linearity assumption is not well validated. We conducted a matched case-control study in children from the Chronic Kidney Disease in Children (CKiD) cohort ages 1-16 years with mild to moderate CKD to assess whether GFR decline follows a nonlinear trajectory as CKD approaches ESRD. Children (n=125) who initiated RRT (cases) during follow-up were individually matched by CKD stage at baseline and glomerular/nonglomerular diagnosis with children (n=125) who remained RRT-free when the corresponding case initiated RRT (controls). GFR trajectories were compared using log-linear and piecewise log-linear mixed effects models adjusted for baseline characteristics. From study entry to 18 months before RRT, GFR declined 7% faster among cases compared with controls. However, GFR declined 26% faster among cases compared with controls (P<0.001) during the 18 months before RRT. Nonlinearity in the rate of kidney function loss, which was shown in this cohort, may preclude accurate clinical prediction of the timing of RRT and adequate patient preparation. This study should prompt the characterization of predictive factors that may contribute to an acceleration of kidney function decline.

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          Author and article information

          Journal
          J. Am. Soc. Nephrol.
          Journal of the American Society of Nephrology : JASN
          American Society of Nephrology (ASN)
          1533-3450
          1046-6673
          May 2014
          : 25
          : 5
          Affiliations
          [1 ] Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland;
          [2 ] Department of Pediatrics, University of Rochester School of Medicine, Rochester, New York;
          [3 ] Department of Pediatrics, Children's Mercy Hospital and Clinics, Kansas City, Missouri; and.
          [4 ] Departments of Pediatrics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
          [5 ] Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; aabraham@jhsph.edu.
          Article
          ASN.2013050487
          10.1681/ASN.2013050487
          4005299
          24385591
          0774205b-b2e2-46df-bce3-1833d5c49baa
          History

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