Literature DB >> 15882252

Definition and classification of chronic kidney disease: a position statement from Kidney Disease: Improving Global Outcomes (KDIGO).

Andrew S Levey1, Kai-Uwe Eckardt, Yusuke Tsukamoto, Adeera Levin, Josef Coresh, Jerome Rossert, Dick De Zeeuw, Thomas H Hostetter, Norbert Lameire, Garabed Eknoyan.   

Abstract

Chronic kidney disease (CKD) is a worldwide public health problem, with adverse outcomes of kidney failure, cardiovascular disease (CVD), and premature death. A simple definition and classification of kidney disease is necessary for international development and implementation of clinical practice guidelines. Kidney Disease: Improving Global Outcomes (KDIGO) conducted a survey and sponsored a controversies conference to (1) provide a clear understanding to both the nephrology and nonnephrology communities of the evidence base for the definition and classification recommended by Kidney Disease Quality Outcome Initiative (K/DOQI), (2) develop global consensus for the adoption of a simple definition and classification system, and (3) identify a collaborative research agenda and plan that would improve the evidence base and facilitate implementation of the definition and classification of CKD. The K/DOQI definition and classification were accepted, with clarifications. CKD is defined as kidney damage or glomerular filtration rate (GFR) <60 mL/min/1.73 m(2) for 3 months or more, irrespective of cause. Kidney damage in many kidney diseases can be ascertained by the presence of albuminuria, defined as albumin-to-creatinine ratio >30 mg/g in two of three spot urine specimens. GFR can be estimated from calibrated serum creatinine and estimating equations, such as the Modification of Diet in Renal Disease (MDRD) Study equation or the Cockcroft-Gault formula. Kidney disease severity is classified into five stages according to the level of GFR. Kidney disease treatment by dialysis and transplantation should be noted. Simple, uniform classifications of CKD by cause and by risks for kidney disease progression and CVD should be developed.

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Year:  2005        PMID: 15882252     DOI: 10.1111/j.1523-1755.2005.00365.x

Source DB:  PubMed          Journal:  Kidney Int        ISSN: 0085-2538            Impact factor:   10.612


  896 in total

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Journal:  Am J Hematol       Date:  2011-05-31       Impact factor: 10.047

2.  Aristolactam-DNA adducts are a biomarker of environmental exposure to aristolochic acid.

Authors:  Bojan Jelaković; Sandra Karanović; Ivana Vuković-Lela; Frederick Miller; Karen L Edwards; Jovan Nikolić; Karla Tomić; Neda Slade; Branko Brdar; Robert J Turesky; Želimir Stipančić; Damir Dittrich; Arthur P Grollman; Kathleen G Dickman
Journal:  Kidney Int       Date:  2011-11-09       Impact factor: 10.612

3.  A Case for Early Screening for Diabetic Kidney Disease.

Authors:  Adam Whaley-Connell; Kunal Chaudhary; Madhukar Misra; Ramesh Khanna
Journal:  Cardiorenal Med       Date:  2011-10-05       Impact factor: 2.041

4.  Evaluation of estimated creatinine clearance before steady state in acute kidney injury by creatinine kinetics.

Authors:  Masatomo Yashiro; Miyuki Ochiai; Nao Fujisawa; Yuko Kadoya; Tadashi Kamata
Journal:  Clin Exp Nephrol       Date:  2012-02-14       Impact factor: 2.801

5.  Estimated GFR, albuminuria, and complications of chronic kidney disease.

Authors:  Lesley A Inker; Josef Coresh; Andrew S Levey; Marcello Tonelli; Paul Muntner
Journal:  J Am Soc Nephrol       Date:  2011-09-30       Impact factor: 10.121

6.  Population-based risk assessment of APOL1 on renal disease.

Authors:  David J Friedman; Julia Kozlitina; Giulio Genovese; Prachi Jog; Martin R Pollak
Journal:  J Am Soc Nephrol       Date:  2011-10-13       Impact factor: 10.121

Review 7.  Validation of CKD and related conditions in existing data sets: A systematic review.

Authors:  Morgan E Grams; Laura C Plantinga; Elizabeth Hedgeman; Rajiv Saran; Gary L Myers; Desmond E Williams; Neil R Powe
Journal:  Am J Kidney Dis       Date:  2010-08-06       Impact factor: 8.860

8.  For estimating creatinine clearance measuring muscle mass gives better results than those based on demographics.

Authors:  Andrew D Rule; Kent R Bailey; Gary L Schwartz; Sundeep Khosla; John C Lieske; L Joseph Melton
Journal:  Kidney Int       Date:  2009-01-28       Impact factor: 10.612

9.  CKD surveillance using laboratory data from the population-based National Health and Nutrition Examination Survey (NHANES).

Authors:  Alejandro F Castro; Josef Coresh
Journal:  Am J Kidney Dis       Date:  2009-03       Impact factor: 8.860

10.  Cystatin C level as a marker of kidney function in human immunodeficiency virus infection: the FRAM study.

Authors:  Michelle C Odden; Rebecca Scherzer; Peter Bacchetti; Lynda Anne Szczech; Stephen Sidney; Carl Grunfeld; Michael G Shlipak
Journal:  Arch Intern Med       Date:  2007-11-12
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