Estimates Of Glomerular Filtration Rate Based On Creatinine And Cystatin C Equations In Critically Ill Patients


Estimated Glomerular Filtration Rate
Serum Creatinine
Serum Cystatin C
Intensive Care Unit



Introduction: Accurate assessment of renal function in the critically ill is important for diagnostic and prognostic performance. It is usually estimated from various estimate glomerular filtration rate (eGFR) equations. We evaluated eGFR based on Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) from serum creatinine (SCr), serum cystatin C (SCysC), and its combination, against 24-hour creatinine clearance (CrCL) to find the most accurate, precise, and less biased equation for GFR estimation.

Methods: Critically ill patients, older than 18 years who stayed longer than 24-hours were included. Urinary creatinine, SCr, and SCysC were measured at three-time intervals (8, 24, and 72-hour). After estimating GFR from SCr(eGFRCr), SCysC, (eGFRCysC) and combined CKD-EPI (eGFRCr-CysC), results were compared with CrCL.

Results: A total of 43 patients were recruited. eGFRCr had the highest correlation to CrCL, with correlation of 0.81 and 0.73 at 24 hrs and 72 hrs, respectively, and was the most precise and accurate equation compared to eGFRCysC and eGFRCr-CysC at all-time intervals. The bias was lowest in eGFRCysC equation. The Area Under Curve of eGFRCr in diagnosing acute kidney injury (AKI) was 0.93 and 0.84 at 24 and 72 hours, respectively. Neither CKD-EPI equations nor CrCL played a role in the prediction of in-hospital mortality with p>0.05 at all-time points.

Conclusion: eGFRCr had the highest correlation to CrCL and was the most accurate and precise equation, however, eGFRCysC had lowest bias at all-time intervals. Most of the equations contributed to the diagnosis of AKI. However, none on contributed to prediction of in-hospital mortality.  




Diego, E., Castro, P., Soy, D., et al. Predictive performance of glomerular filtration rate estimation equations based on cystatin C versus serum creatinine values in critically ill patients. American Journal of Health-System Pharmacy. 2016;73(4):206–215.

Kher, V., Srisawat, N., Noiri, E., et al. Prevention and Therapy of Acute Kidney Injury in the Developing World. Kidney International Reports. 2017;2(4):544–558.

Willey, J. Z., Moon, Y. P., Ali Husain, S., et al. Creatinine versus cystatin C for renal function-based mortality prediction in an elderly cohort: The Northern Manhattan study. PLoS ONE. 2020;15(1):1–26.

Gaspari, F., Ruggenenti, P., Porrini, E., et al. The GFR and GFR decline cannot be accurately estimated in type 2 diabetics. Kidney International. 2013;84(1):164–173.

Bragadottir, G., Redfors, B., & Ricksten, S. E. Assessing glomerular filtration rate (GFR) in critically ill patients with acute kidney injury - true GFR versus urinary creatinine clearance and estimating equations. Critical Care. 2013;17(3):R108.

Zou, L. X., Sun, L., Nicholas, S. B., et al. Comparison of bias and accuracy using cystatin C and creatinine in CKD-EPI equations for GFR estimation. European Journal of Internal Medicine. 2013;80(June):29–34.

Bjornstad, P., Karger, A. B., & Maahs, D. M. Measured GFR in Routine Clinical Practice—The Promise of Dried Blood Spots. Advances in Chronic Kidney Disease. 2018;25(1):6–83.

Hu, J., Xu, X., Zhang, K., Li, Y., Zheng, J., et al. Comparison of estimated glomerular filtration rates in Chinese patients with chronic kidney disease among serum creatinine-, cystatin-C- and creatinine-cystatin-C-based equations: A retrospective cross-sectional study. Clinica Chimica Acta. 2020;505(February):34–42.

Ferguson, T. W., Komenda, P., & Tangri, N. Cystatin C as a biomarker for estimating glomerular filtration rate. Current Opinion in Nephrology and Hypertension. 2015;24(3):295–300.

Liao, Y., Liao, W., Liu, J., et al. Assessment of the CKD-EPI equation to estimate glomerular filtration rate in adults from a Chinese CKD population. Journal of International Medical Research. 201139(6):2273–2280.

Seegmiller, J. C., Eckfeldt, J. H., & Lieske, J. C. Challenges in Measuring Glomerular Filtration Rate: A Clinical Laboratory Perspective. Advances in Chronic Kidney Disease. 2018;25(1):84–92.

Liu, W. S., Chung, Y. T., Yang, C. Y., et al. Serum Creatinine Determined by Jaffe, Enzymatic Method, and Isotope Dilution-Liquid Chromatography-Mass Spectrometry in Patients Under Hemodialysis. Journal of Clinical Laboratory Analysis. 2012;26(3):206–214.

Puzantian, H. V., & Townsend, R. R. Understanding kidney function assessment: the basics and advances. Journal of the American Association of Nurse Practitioners. 2013;25(7):334–341.

Segarra, A., de la Torre, J., Ramos, N., et al. Assessing glomerular filtration rate in hospitalized patients: A comparison between CKD-EPI and four cystatin c-based equations. Clinical Journal of the American Society of Nephrology. 2011;6(10):2411–2420.

Jalalonmuhali, M., Elagel, S. M. A., Tan, et al. Estimating Renal Function in the Elderly Malaysian Patients Attending Medical Outpatient Clinic: A Comparison between Creatinine Based and Cystatin-C Based Equations. International Journal of Nephrology, 2018.

Sans, L., Radosevic, A., Quintian, C., et al. Cystatin C estimated glomerular filtration rate to assess renal function in early stages of autosomal dominant polycystic kidney disease. PLoS ONE. 2017;12(3):1–10.

Khalid, U., Haroon, Z. H., Aamir, M., et al. Comparison of estimated glomerular filtration rate with both serum creatinine and cystatin c (eGFRcr-cys) versus single analyte (eGFRcr or eGFRcys) using CKD-EPI and MDRD equations in tertiary care hospital settings. Journal of the College of Physicians and Surgeons Pakistan. 2020;30(7):701–706.

Ji, M., Lee, Y. H., Hur, M., Kim, H., et al. Comparing results of five glomerular filtration rate-estimating equations in the Korean general population: MDRD Study, revised Lund-Malmö, and three CKD-EPI equations. Annals of Laboratory Medicine. 2016; 36(6):521–528.

Lamb, E. J., & Stevens, P. E. Estimating and measuring glomerular filtration rate: Methods of measurement and markers for estimation. Current Opinion in Nephrology and Hypertension. 2014;23(3):258–266.

Yang, M., Zou, Y., Lu, T., Nan, Y., Niu, J., Du, X., & Gu, Y. (2019). Revised Equations to Estimate Glomerular Filtration Rate from Serum Creatinine and Cystatin C in China. Kidney and Blood Pressure Research. 2019;44(4), 553-564.

Keddis, M. T., Amer, H., Voskoboev, N., et al. Article Creatinine – Based and Cystatin C – Based GFR Estimating Equations and Their Non-GFR Determinants. Kidney Transplant Recipients. 2016:1–10.

Ravn, B., Rimes-Stigare, C., Bell, M., et al. Creatinine versus cystatin C based glomerular filtration rate in critically ill patients. Journal of Critical Care. 2019;52:136–140.

Xue, Y., Wang, Q.-S., Chi, X.-H., et al. CKD-EPI creatinine-cystatin C glomerular filtration rate estimation equation seems more suitable for Chinese patients with chronic kidney disease than other equations. BMC Nephrology. 2017;18(1)

Wiersema, R., Jukarainen, S., Eck, R. J., et al. Different applications of the KDIGO criteria for AKI lead to different incidences in critically ill patients: A post hoc analysis from the prospective observational SICS-II study. Critical Care. 2020;24(1):1–8.

Bongiovanni, C., Magrini, L., Salerno, G., Gori, C et al. Serum Cystatin C for the Diagnosis of Acute Kidney Injury in Patients Admitted in the Emergency Department. Disease Markers. 2015.

Pickering, J. W., Frampton, C. M., Walker, R. J., S et al. Four hour creatinine clearance is better than plasma creatinine for monitoring renal function in critically ill patients. Critical Care. 2012;16(3):R107.

Jo, J. Y., Ryu, S. A., Kim, J. Il, et al. Comparison of five glomerular filtration rate estimating equations as predictors of acute kidney injury after cardiovascular surgery. Scientific Reports. 2019;9(1):1–9.

Willey, J. Z., Moon, Y. P., Ali Husain, S., et al. Creatinine versus cystatin C for renal function-based mortality prediction in an elderly cohort: The Northern Manhattan study. PLoS ONE. 2020;15(1);1–26

Targher, G., Zoppini, G., Mantovani, W., Chonchol, M., Negri, C., Stoico, V., Mantovani, A., De Santi, F., & Bonora, E. Comparison of two creatinine-based estimating equations in predicting all-cause and cardiovascular mortality in patients with type 2 diabetes. Diabetes Care. 2012;35(11):2347–2353.

McFadden, E. C., Hirst, J. A., Verbakel, J. Y., et al. Systematic review and metaanalysis comparing the bias and accuracy of the modification of diet in renal disease and chronic kidney disease epidemiology collaboration equations in community-based populations. Clinical Chemistry. 2018;64(3):475–485.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright (c) 2022 Journal of Clinical and Translational Nephrology