Literature DB >> 21115622

Implications of the CKD-EPI GFR estimation equation in clinical practice.

Jesse D Schold1, Sankar D Navaneethan, Stacey E Jolly, Emilio D Poggio, Susana Arrigain, Welf Saupe, Anil Jain, John W Sharp, James F Simon, Martin J Schreiber, Joseph V Nally.   

Abstract

BACKGROUND AND OBJECTIVES: Chronic kidney disease (CKD) is a significant public health problem whose diagnosis and staging relies upon GFR-estimating equations, including the new CKD-EPI equation. CKD-EPI demonstrated superior performance compared with the existing MDRD equation but has not been applied to a healthcare system. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: We identified 53,759 patients with stages 3 to 5 CKD on the basis of either MDRD or CKD-EPI equations using two eGFR values <60 ml/min per 1.73 m² > 90 days apart from an outpatient setting. We compared patient characteristics, presence of related diagnosis codes, and time CKD classification between equations.
RESULTS: The number of patients identified with CKD decreased 10% applying CKD-EPI versus MDRD. Changes varied substantially by patient characteristics including a 35% decrease among patients < 60 years and a 10% increase among patients > 90 years. Women, non-African Americans, nondiabetics, and obese patients were less likely to be classified on the basis of CKD-EPI. Time to CKD classification was significantly longer with CKD-EPI among younger patients. 14% of patients identified with CKD on the basis of either estimating equation also had a related ICD-9 diagnosis, ranging from 19% among patients < 60 years to 7% among patients > 90 years.
CONCLUSIONS: Consistent with findings in the general population, CKD-EPI resulted in substantial declines in equation-based CKD diagnoses in a large healthcare system. Further research is needed to determine whether widespread use of CKD-EPI with current guidelines could lead to delayed needed care among younger patients or excessive referrals among older patients.

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Year:  2010        PMID: 21115622      PMCID: PMC3082406          DOI: 10.2215/CJN.04240510

Source DB:  PubMed          Journal:  Clin J Am Soc Nephrol        ISSN: 1555-9041            Impact factor:   8.237


  22 in total

1.  A software upgrade: CKD testing in 2010.

Authors:  Bryan N Becker; Joseph A Vassalotti
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2.  Establishing a national chronic kidney disease surveillance system for the United States.

Authors:  Rajiv Saran; Elizabeth Hedgeman; Laura Plantinga; Nilka Rios Burrows; Brenda W Gillespie; Eric W Young; Josef Coresh; Meda Pavkov; Desmond Williams; Neil R Powe
Journal:  Clin J Am Soc Nephrol       Date:  2009-12-03       Impact factor: 8.237

3.  Development and validation of an electronic health record-based chronic kidney disease registry.

Authors:  Sankar D Navaneethan; Stacey E Jolly; Jesse D Schold; Susana Arrigain; Welf Saupe; John Sharp; Jennifer Lyons; James F Simon; Martin J Schreiber; Anil Jain; Joseph V Nally
Journal:  Clin J Am Soc Nephrol       Date:  2010-11-04       Impact factor: 8.237

Review 4.  Early recognition and prevention of chronic kidney disease.

Authors:  Matthew T James; Brenda R Hemmelgarn; Marcello Tonelli
Journal:  Lancet       Date:  2010-04-10       Impact factor: 79.321

5.  A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group.

Authors:  A S Levey; J P Bosch; J B Lewis; T Greene; N Rogers; D Roth
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6.  Comparison of the prevalence and mortality risk of CKD in Australia using the CKD Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) Study GFR estimating equations: the AusDiab (Australian Diabetes, Obesity and Lifestyle) Study.

Authors:  Sarah L White; Kevan R Polkinghorne; Robert C Atkins; Steven J Chadban
Journal:  Am J Kidney Dis       Date:  2010-04       Impact factor: 8.860

Review 7.  Clinical epidemiology of cardiovascular disease in chronic kidney disease prior to dialysis.

Authors:  Adeera Levin
Journal:  Semin Dial       Date:  2003 Mar-Apr       Impact factor: 3.455

8.  National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification.

Authors:  Andrew S Levey; Josef Coresh; Ethan Balk; Annamaria T Kausz; Adeera Levin; Michael W Steffes; Ronald J Hogg; Ronald D Perrone; Joseph Lau; Garabed Eknoyan
Journal:  Ann Intern Med       Date:  2003-07-15       Impact factor: 25.391

9.  A new equation to estimate glomerular filtration rate.

Authors:  Andrew S Levey; Lesley A Stevens; Christopher H Schmid; Yaping Lucy Zhang; Alejandro F Castro; Harold I Feldman; John W Kusek; Paul Eggers; Frederick Van Lente; Tom Greene; Josef Coresh
Journal:  Ann Intern Med       Date:  2009-05-05       Impact factor: 25.391

10.  Associations of resting heart rate with insulin resistance, cardiovascular events and mortality in chronic kidney disease.

Authors:  Srinivasan Beddhu; Sagar U Nigwekar; Xilulian Ma; Tom Greene
Journal:  Nephrol Dial Transplant       Date:  2009-03-22       Impact factor: 5.992

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  9 in total

1.  The prevalence of chronic kidney disease in the general population in Romania: a study on 60,000 persons.

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Journal:  Int Urol Nephrol       Date:  2011-03-02       Impact factor: 2.370

2.  Increased risk of cardiovascular events in end-stage renal disease patients with osteoporosis: a nationwide population-based cohort study.

Authors:  T-M Yu; C-L Lin; K-H Shu; Y-L Liu; C-H Chen; S-T Huang; C-H Kao
Journal:  Osteoporos Int       Date:  2014-12-10       Impact factor: 4.507

3.  Impact on cardiovascular risk follow-up from a shift to the CKD-EPI formula for eGFR reporting: a cross-sectional population-based primary care study.

Authors:  Vincent A van Gelder; Nynke D Scherpbier-de Haan; Wim J C de Grauw; Christopher A O'Callaghan; Jack F M Wetzels; Daniel S Lasserson
Journal:  BMJ Open       Date:  2013-09-25       Impact factor: 2.692

4.  SUDOSCAN, an effective tool for screening chronic kidney disease in patients with type 2 diabetes.

Authors:  Fei Mao; Siying Liu; Xiaona Qiao; Hangping Zheng; Qian Xiong; Jie Wen; Shuo Zhang; Zhaoyun Zhang; Hongying Ye; Hongli Shi; Bin Lu; Yiming Li
Journal:  Exp Ther Med       Date:  2017-06-27       Impact factor: 2.447

5.  Impact of a single eGFR and eGFR-estimating equation on chronic kidney disease reclassification: a cohort study in primary care.

Authors:  Jennifer A Hirst; Maria Dla Vazquez Montes; Clare J Taylor; José M Ordóñez-Mena; Emma Ogburn; Vanshika Sharma; Brian Shine; Tim James; Fd Richard Hobbs
Journal:  Br J Gen Pract       Date:  2018-07-02       Impact factor: 5.386

6.  Creatinine-or cystatin C-based equations to estimate glomerular filtration in the general population: impact on the epidemiology of chronic kidney disease.

Authors:  Pierre Delanaye; Etienne Cavalier; Olivier Moranne; Laurence Lutteri; Jean-Marie Krzesinski; Olivier Bruyère
Journal:  BMC Nephrol       Date:  2013-03-12       Impact factor: 2.388

7.  Electronic health records: a new tool to combat chronic kidney disease?

Authors:  Sankar D Navaneethan; Stacey E Jolly; John Sharp; Anil Jain; Jesse D Schold; Martin J Schreiber; Joseph V Nally
Journal:  Clin Nephrol       Date:  2013-03       Impact factor: 0.975

Review 8.  Using race in the estimation of glomerular filtration rates: time for a reversal?

Authors:  Heather Morris; Sumit Mohan
Journal:  Curr Opin Nephrol Hypertens       Date:  2020-03       Impact factor: 3.416

9.  The Impact of Admission Serum Creatinine on Major Adverse Clinical Events in ST-Segment Elevation Myocardial Infarction Patients Undergoing Primary Percutaneous Coronary Intervention.

Authors:  Poornima Vinod; Taylor Kann; Shyam Polaconda; Alibel Bello; Mohamed Khayata; Fernando Munoz; Vinod Krishnappa; Rupesh Raina
Journal:  Cardiol Res       Date:  2018-04-25
  9 in total

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