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Evaluation of kidney disease using e-GFR compared to measured creatinine clearance



SP Tejaswi Pullakanam*, 1, Barla Krishna1, Nakka Madhuri1, Priya K. Dhas2 & Nekkala Ramakrishna1



1Department of Biochemistry, Gayatri Vidya Parishad Institute of Health Care and Medical Technology, Marikavalasa, Visakhapatnam, Andhra Pradesh, India; 2Department of Biochemistry, Vinayaka Missions Kirupananda Variyar Medical College and Hospitals, Salem, Tamilnadu, India; *Corresponding author



S.P. Tejaswi Pullakanam - E-mail:tejaswibio22@gmail.com

Barla Krishna - E-mail: drkrishnabarla@gmail.com

Nakka Madhuri - E-mail: nmadhuri@gmail.com

Priya K Dhas - E-mail: priyakdhas79@gmail.com

Nekkala Ramakrishna - E-mail: dr.ramakrishnanekkala@gmail.com


Article Type

Research Article



Received March 1, 2024; Revised March 31, 2024; Accepted March 31, 2024, Published March 31, 2024



Measurement of renal function is required for diagnosis and stratification of kidney disease. GFR is considered as the best overall measure of kidney function for diagnosis and treatment of patients with CKD. Measuring GFR is time consuming and hence eGFR is calculated using equations with endogenous markers like SCr. Therefore, it is of interest to examine the accuracy of creatinine based estimates (CrCl and CG formula) of GFR among patients. Thus, 60 in-patients (30 men and 30 women) at the GVP hospital and 40 controls were enrolled in the study. SCr and 24 hrs urine creatinine are estimated using blood sample and same day 24-hr urine collection. SCr is estimated using the Kinetic Jaffe’s method in Auto analyzer for serum and urine. eGFR is calculated using the CG formula for the SCr value. We evaluated the correlation between measured CrCl derived from 24-hr urine collection and calculated/predicted CrCl using the CG equations. A positive correlation was observed between measured GFR and e-GFR in case and control groups.



Serum creatinine (SCr), creatinine clearance (CrCl), glomerular filtration rate (GFR), Cockcroft–Gault formula (CG), chronic kidney disease (CKD)



Pullakanam et al. Bioinformation 20(3): 229-233 (2024)


Edited by

Peter N Pushparaj






Biomedical Informatics



This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.