Estimating Glomerular Filtration Rate Using the Chronic Kidney Disease-Epidemiology Collaboration Creatinine Equation

Abstract

Serum creatinine is measured more than 280 million times annually in the United States, and more than 80% of clinical laboratories now report an estimated glomerular filtration rate (GFR) when serum creatinine is measured.1,2 The most commonly used equation is the Modification of Diet and Renal Disease (MDRD) Study equation. Recently, the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) developed and validated a new equation, the CKD-EPI creatinine equation, which uses the same variables as the MDRD Study but is more accurate compared with measured GFR2,3; however, as for other diagnostic tests, other criteria are also important in clinical practice and public health, including detecting disease and predicting prognosis. Article see p 309 In this issue of Circulation: Heart Failure , McAlister and colleagues compare the CKD-EPI and MDRD Study equations for estimating prevalence of chronic kidney disease (CKD) and predicting mortality in a pooled individual patient dataset from 25 studies of 20 754 patients with heart failure included in the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC).4 CKD was defined as estimated glomerular filtration rate (eGFR) <60 mL/min per 1.73 m2 Mortality was defined as incidence per 1000 person years. During the average follow-up interval of 2.0 years, 4981 patients died. The authors showed that the CKD-EPI equation reclassified more people to lower than higher eGFR categories and more accurately predicted mortality risk than the MDRD Study equation. The finding of more accurate risk prediction using the CKD-EPI equation is consistent with previously published studies comparing the 2 equations for prediction of adverse outcomes (Table)4–9; however, in most other studies, reclassification to higher eGFR categories was more common than reclassification to lower eGFR categories. Understanding these findings requires some discussion of the GFR estimating equations …