International Urology and Nephrology

, Volume 51, Issue 1, pp 139–146 | Cite as

A comparison between 2017 FAS and 2012 CKD-EPI equations: a multi-center validation study in Chinese adult population

  • Zhenzhu Yong
  • Fen Li
  • Xiaohua Pei
  • Xun Liu
  • Dan Song
  • Xiaoxuan Zhang
  • Weihong ZhaoEmail author
Nephrology - Original Paper



The recent guidelines recommend using the estimated glomerular filtration rate (eGFR) to evaluate renal function. There are two reported full-age-spectrum (FAS) equations in 2017, which are based on serum cystatin C concentrations with or without accompanying serum creatinine level (FASCr–Cys or FASCys). We compared the performance and assessed the applicability of the new FAS equation with the 2012 CKD-EPI (CKD-EPICys and CKD-EPICr–Cys) equation in Chinese subjects.


A total of 1184 patients, mean aged 55.06 year who underwent 99mTc-DTPA GFR measurements (rGFR) from four hospitals were enrolled. The bias (eGFR-rGFR), precision (interquartile range of difference [IQR]), and accuracy (the proportion of eGFR within 30% of rGFR [P30]) of eGFR and rGFR calculated by four equations were compared.


Generally, the equation based on the combination of Cys and Scr performed superior to that on the basis of Cys alone, either the CKD-EPICr–Cys or the FASCr–Cys. Detailedly, referred to rGFR (67.33 ml/min/1.73 m2), the CKD-EPICys, CKD-EPICr–Cys, FASCys, and the FASCr–Cys estimated GFR 56.46 ml/min/1.73 m2, 62.79 ml/min/1.73 m2, 56.45 ml/min/1.73 m2, and 61.04 ml/min/1.73 m2, gave ROCAUC0.944, 0.954, 0.943, and 0.953, respectively. Another comparison as to bias, precision, P30, and RMSE with FASCr–Cys were − 2.87 ml/min/1.73 m2, 19.01 ml/min/1.73 m2, 74.16%, and 17.84 ml/min/1.73 m2 showed that FASCr–Cys performed approximately more accurate than other equations, as well as the diagnostic consistency of GFR staging. In the rGFR < 60 ml/min/1.73 m2 subgroup, the FASCr–Cys equation showed the best performance. In older subjects, compared with FASCys, CKD-EPICr–Cys, and CKD-EPICys, the FASCr–Cys equation had relatively less bias (− 8.09 vs. − 9.63, − 7.52, − 11.04, P < 0.05), most precise (15.18 vs. 16.32, 15.22, 16.63), and most accuracy, P30 was statistically different from the other equations, and achieved a ideal value > 70%.


The performance of the FASCr–Cys equation is better than that of the CKD-EPICr–Cys equation in the Chinese population, particularly in the elderly. Yet, further modification of FAS equations from a large-scale study could be more suitable for the Chinese population, particularly in older people.


Glomerular filtration rate (GFR) Creatinine Cystatin C Estimating equation Full-age-spectrum 



This work was supported by the grants from the National Natural Science Foundation of China H0511-81670677, Clinical Medicine Research Special Funds of Chinese Medical Association 15020020590, Jiangsu Provincial Key Discipline of Medicine ZDXKA2016003, Jiangsu Provincial Key Laboratory of Geriatrics, Jiangsu Province’s Key Medical Talents Program ZDRCA2016021, Jiangsu Province 333 Project BRA2017409, Jiangsu Province’s Key Medical Young Talents Program QNRC2016592, and Jiangsu cadres health care research BJ16016, BJ17018.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the ethics committee of the First Affiliated Hospital of Nanjing Medical University and conducted in accordance with the Declaration of Helsinki.


  1. 1.
    Murphy D, McCulloch CE, Lin F et al (2016) Trends in prevalence of chronic kidney disease in united states. Ann Intern Med 165(7):473–481CrossRefGoogle Scholar
  2. 2.
    deBoer IH (2012) Chronic kidney disease—a challenge for all ages. JAMA 308(22):2401–2402CrossRefGoogle Scholar
  3. 3.
    Stevens LA, Coresh J, Levey AS (2008) CKD in the elderly—old questions and new challenges. Am J Kidney Dis 51(3):353–357CrossRefGoogle Scholar
  4. 4.
    Zhang L, Wang F, Wang L et al (2012) Prevalence of chronic kidney disease in China: a cross-sectional survey. Lancet 379(9818):815–822CrossRefGoogle Scholar
  5. 5.
    Levey AS, Inker LA, Coresh J (2014) GFR estimation: from physiology to public health. Am J Kidney Dis 63:820–834CrossRefGoogle Scholar
  6. 6.
    Levey AS, Becker C, Inker LA (2015) Glomerular filtration rate and albuminuria for detection and staging of acute and chronic kidney disease in adults: a systematic review. JAMA 313(8):837–846CrossRefGoogle Scholar
  7. 7.
    Soveri I, Berg UB, Bjork J et al (2014) Measuring GFR: a systematic review. Am J Kidney Dis 64:411–424CrossRefGoogle Scholar
  8. 8.
    Delpassand ES, Homayoon K, Madden T et al (2000) Determination of glomerular filtration rate using a dual-detectorgamma camera and the geometric mean of renal activity: correlation with the Tc-99m DTPA plasma clearance method. Clin Nucl Med 25(4):258–262CrossRefGoogle Scholar
  9. 9.
    Orsal E, Seben B, Subasi ID et al (2013) Vesicoureteral refluxin a nonfunctioning kidney detected by 99mTc-DTPA study. Jpn J Radiol 31(12):823–825CrossRefGoogle Scholar
  10. 10.
    Amin A, El-Sayed S, Taher N et al (2012) Tc-99m diethylenetriamine pentaacetic acid (DTPA) renal function reserve estimation: is it a reliable predictive tool for assessment of preclinical renal involvement in scleroderma patients. Clin Rheumatol 31:961–966CrossRefGoogle Scholar
  11. 11.
    Inker LA, Schmid CH, Tighiouart H et al (2012) Estimating glomerular fltration rate from serum creatinine and cystatin C. N Engl J Med 367(1):20–29CrossRefGoogle Scholar
  12. 12.
    Farrington K, Covic A, Aucella F et al (2016) Clinical practice guideline on management of older patients with chronic kidney disease stage 3b or higher (eGFR < 45 ml/min/1.73 m2). Nephrol Dial Transplant 31(suppl 2):ii1–ii66CrossRefGoogle Scholar
  13. 13.
    Fan L, Inker LA, Rossert J et al (2014) Glomerular filtration rate estimation using cystatin C alone or combined with creatinine as a cofirmatory test. Nephrol Dial Transplant 29:1195–1203CrossRefGoogle Scholar
  14. 14.
    Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group (2013) KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 3:1–150CrossRefGoogle Scholar
  15. 15.
    Fan L, Levey AS, Gudnason V et al (2015) Comparing GFR estimating equations using cystatin C and creatinine in elderly individuals. J Am Soc Nephrol 26(8):982–1989CrossRefGoogle Scholar
  16. 16.
    Kong X, Ma Y, Chen J et al (2013) Evaluation of the Chronic Kidney Disease Epidemiology Collaboration equation for estimating glomerular filtration rate in the Chinese population. Nephrol Dial Transplant 28(3):641–651CrossRefGoogle Scholar
  17. 17.
    Barr EL, Maple-Brown LJ, Barzi F et al (2017) Comparison of creatinine and cystatin C based eGFR in the estimation of glomerular filtration rate in Indigenous Australians: the eGFR Study. Clin Biochem 50(6):301–308CrossRefGoogle Scholar
  18. 18.
    Zhu Y, Ye XS, Zhu B et al (2014) Comparisons between the 2012 New CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equations and other four approved equations. PLoS ONE 9(1):e84688CrossRefGoogle Scholar
  19. 19.
    Li F, Pei XH, Ye XS et al (2017) Modification of the 2012 CKD-EPI equations for the elderly Chinese. Int Urol Nephrol 49(3):467–473CrossRefGoogle Scholar
  20. 20.
    Pottel H, Delanaye P, Schaeffner E et al (2017) Estimating glomerular filtration rate for the full age spectrum from serum creatinine and cystatin C. Nephrol Dial Transplant 32(3):497–507Google Scholar
  21. 21.
    Pottel H, Hoste L, Yayo E et al (2017) Glomerular filtration rate in healthy living potential kidney donors: a meta-analysis supporting the construction of the full age spectrum equation. Nephron 135(2):105–119CrossRefGoogle Scholar
  22. 22.
    Kilbride HS, Stevens PE, Eaglestone G et al (2013) Accuracy of the MDRD (modification of diet in renal disease) study and CKD-EPI (CKD epidemiology collaboration) equations for estimation of GFR in the elderly. Am J Kidney Dis 61:57–66CrossRefGoogle Scholar
  23. 23.
    Alshaer IM, Kilbride HS, Stevens PE et al (2014) External validation of the Berlin equations for estimation of GFR in the elderly. Am J Kidney Dis 63:862–865CrossRefGoogle Scholar
  24. 24.
    Vidal-Petiot E, Haymann JP, Letavernier E et al (2014) External validation of the BIS (Berlin initiative study)-1 GFR estimating equation in the elderly. Am J Kidney Dis 63:865–867CrossRefGoogle Scholar
  25. 25.
    Schaeffner ES, Ebert N, Delanaye P et al (2012) Two novel equations to estimate kidney function in persons aged 70 years or older. Ann Intern Med 157:471–481CrossRefGoogle Scholar
  26. 26.
    Stevens LA, Coresh J, Schmid CH et al (2008) Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3418 individuals with CKD. Am J Kidney Dis 51(3):395–406CrossRefGoogle Scholar
  27. 27.
    Ma YC, Zuo L, Chen L et al (2010) Distribution of measured GFR in apparently healthy Chinese adults. Am J Kidney Dis 56(2):420–421CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  • Zhenzhu Yong
    • 1
  • Fen Li
    • 1
  • Xiaohua Pei
    • 1
  • Xun Liu
    • 2
  • Dan Song
    • 3
  • Xiaoxuan Zhang
    • 4
  • Weihong Zhao
    • 1
    Email author
  1. 1.Department of Geriatric NephrologyThe First Affiliated Hospital of Nanjing Medical UniversityNanjingPeople’s Republic of China
  2. 2.Department of NephrologyThe Third Affiliated Hospital of Sun Yat-sen UniversityGuangzhouPeople’s Republic of China
  3. 3.Department of NephrologyThe Affiliated Wuxi No. 2 Hospital of Nanjing Medical UniversityWuxiPeople’s Republic of China
  4. 4.Department of NephrologyThe Fourth Affiliated Hospital of Jilin UniversityChangchunPeople’s Republic of China

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