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  Table of Contents  
Year : 2018  |  Volume : 28  |  Issue : 6  |  Page : 492-493

Estimated glomerular filtration rate using creatinine-based chronic kidney disease epidemiology collaboration equation

Department for Diabetic Kidney Disease, Prof. M. Viswanathan Diabetes Research Centre, M.V. Hospital for Diabetes, Chennai, Tamil Nadu, India

Date of Web Publication14-Dec-2018

Correspondence Address:
V Viswanathan
No. 4, West Madha Church Street, Royapuram, Chennai - 600 013, Tamil Nadu
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/ijn.IJN_439_17

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How to cite this article:
Rani A A, Viswanathan V. Estimated glomerular filtration rate using creatinine-based chronic kidney disease epidemiology collaboration equation. Indian J Nephrol 2018;28:492-3

How to cite this URL:
Rani A A, Viswanathan V. Estimated glomerular filtration rate using creatinine-based chronic kidney disease epidemiology collaboration equation. Indian J Nephrol [serial online] 2018 [cited 2022 Aug 11];28:492-3. Available from:


Recently, we have been reading articles on estimating glomerular filtration rate (eGFR) in Indian population with great interest. With the understanding that there is a rising epidemic of type 2 diabetes mellitus and the subsequent increase in its associated complications, it poses a nationwide threat. Diabetic nephropathy is the significant cause of chronic kidney disease (CKD). An Indian study showed that patients with CKD spend more toward their hospital admission than those without diabetic complications.[1] Hence, glomerular filtration rate (GFR) assessment is important for the clinicians to assess the kidney function, detect and estimate the progression of CKD. eGFR using CKD epidemiology collaboration (CKD-EPI) equation[2] is a major indicator of kidney function, and it plays an important role in detecting, evaluating, and also in managing CKD. Serum creatinine (Scr) or serum cystatin (Scys) is used to estimate GFR. A population-based Indian study emphasized that Cystatin C identifies more patients in early CKD and also in patients with normoalbuminuric CKD when compared to creatinine.[3] This study focused on the creatinine-based equations, such as Cockcroft-Gault and modification of diet in renal disease (MDRD), by comparing it with CKD-EPI equation using Cystatin C. An earlier study by Viswanathan et al.[4] suggested that Cystatin C was a better marker for moderately impaired renal function when compared to creatinine using Cockcroft-Gault. In developing countries like India, use of Cystatin C in clinical practice is limited due to its cost. At present, Cystatin C has an advantage in detecting the early CKD, but it is not a cost-effective test and cannot be recommended for routine clinical practice.

To overcome this limitation, creatinine can be used for eGFR. A recent study in 2017[5] compared the estimation of GFR using gamma camera-based Gates protocol and Scr-based predicting equations with GFR measured by plasma clearance of Tc-99m DTPA in North Indian population. The finding highlighted that CKD-EPI correlated with Tc-99m DTPA and showed least bias and highest precision when compared to GFR estimate using Cockroft-Gault, MDRD, and Gates protocol. Kumpatla et al.[6] compared MDRD equation versus CKD-EPI using Scr and MDRD equation versus CKD-EPI using Scys to estimate eGFR in a clinical setting in South Indian population. The mean bias, mean absolute bias and precision were lesser in MDRD versus CKD-EPI using Scr when compared to that of MDRD versus CKD-EPI using Scys. Likewise, Scr showed highest accuracy when compared with Scys. This showed that creatinine-based CKD-EPI can identify CKD at an early stage. Thus, for an Indian population, CKD-EPI equation using creatinine predicts GFR best than other equations. This underlines the importance of standardization of eGFR calculation among Indian population. Further research is needed in large sample to determine the best methods by comparing eGFR equations with the gold standard methods. In conclusion, CKD-EPI equation using Scr was found to be superior in terms of estimating kidney function and is cost-effective; hence, it can be implemented in the routine clinical practice.

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  References Top

Satyavani K, Kothandan H, Jayaraman M, Viswanathan V. Direct costs associated with chronic kidney disease among type 2 diabetic patients in India. Indian J Nephrol 2014;24:141-7.  Back to cited text no. 1
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Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. Anew equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604-12.  Back to cited text no. 2
Avinash S, Singh VP, Agarwal AK, Chatterjee S, Arya V. Identification and stratification of diabetic kidney disease using serum cystatin C and serum creatinine based estimating equations in type 2 diabetes: A comparative analysis. J Assoc Physicians India 2015;63:28-35.  Back to cited text no. 3
Viswanathan V, Snehalatha C, Nair MB, Ramachandran A. Comparative assessment of cystatin c and creatinine for determining renal function. Indian J Nephrol 2005;15:91-4.  Back to cited text no. 4
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Mulay AV, Gokhale SM. Comparison of serum creatinine-based estimating equations with gates protocol for predicting glomerular filtration rate in Indian population. Indian J Nephrol 2017;27:124-8.  Back to cited text no. 5
[PUBMED]  [Full text]  
Kumpatla S, Soni A, Viswanathan V. Comparison of two creatinine based equations for routine estimation of GFR in a speciality clinic for diabetes. J Assoc Physicians India 2017;65:38-41.  Back to cited text no. 6

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