4.7 Article

National Kidney Foundation's Kidney Disease Outcomes Quality Initiative clinical practice guidelines for chronic kidney disease in children and adolescents: Evaluation, classification, and stratification

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PEDIATRICS
卷 111, 期 6, 页码 1416-1421

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AMER ACAD PEDIATRICS
DOI: 10.1542/peds.111.6.1416

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guidelines; chronic; kidney; children; classification

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Objectives. A series of new guidelines has been developed by the National Kidney Foundation's Kidney Disease Outcomes Quality Initiative to improve the detection and management of chronic kidney disease (CKD). In most instances of CKD, the earliest manifestations of the disorder may be identified by relatively simple tests. Unfortunately, CKD is often under-diagnosed, in part because of the absence of a common definition of CKD and a classification of the stages in its progression. The Kidney Disease Outcomes Quality Initiative clinical practice guidelines for CKD evaluation, classification, and stratification provide a basis to remedy these deficits. The specific goals of the guidelines described in this review are to provide: 1) an overview of the clinical practice guidelines as they pertain to children and adolescents, 2) a simple classification of the stages of CKD, and 3) a practical approach to the laboratory assessment of kidney disease in children and adolescents. Methods. The guidelines were developed as part of an evidence-based evaluation of CKD and its consequences in patients of all ages. The data that were used to generate the guidelines in this article were extracted from a structured analysis of articles that reported on children with CKD. Results and Conclusions. This review presents the definition and 5-stage classification system of CKD developed by the work group assigned to develop the guidelines, and summarizes the major recommendations regarding the early detection of CKD. Major emphasis is placed on the identification of children and adolescents with CKD by measuring the protein-to-creatinine ratio in spot urine specimens and by estimating the glomerular filtration rate from serum creatinine using prediction equations.

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