title: Is the Chronic Kidney Disease Epidemiology Collaboration four-level race equation better than the cystatin C equation?
abstract: OBJECTIVE
  To evaluate the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) four-level race equation in the assessment of glomerular filtration rate (GFR) in Chinese people with chronic kidney disease (CKD), which was published in 2011, compared with the cystatin C-based GFR estimation equation (CysC GFR) and the combination of CysC and serum creatinine equation (CysC-Scr GFR).
  
  
  METHODS
  The CKD-EPI four-level race equation estimated GFR (CKD-EPI GFR) was compared with the CysC GFR and CysC-Scr GFR. Three equations were compared with body surface area (BSA) standardized GFR (sGFR), which was measured by (99m) Tc-DTPA renal dynamic imaging method in 111 CKD cases.
  
  
  RESULTS
  A statistically significant correlation was found between sGFR and CKD-EPI GFR, CysC GFR and CysC-Scr GFR. Three estimated GFR (eGFR) equations of 30% accuracy were 58.6%, 56.8% and 63.5%, respectively. Average deviations of eGFR from sGFR were 2.34, 1.19, and 1.32 (mL/min per 1.73 m(2)) (P > 0.05), respectively. There was no significant deviation in the CKD from stages 1 to 5 in CKD-EPI GFR and CysC-Scr GFR. However, when estimated by CysC GFR, the deviation was increased, with the value of 12.41 mL/min per 1.73 m(2) (P= 0.002) in CKD stage 5.
  
  
  CONCLUSIONS
  Our results showed that in a Chinese population with CKD, CKD-EPI GFR, CysC GFR and CysC-Scr GFR of bias and overall accuracy of 30% were very similar. There was little advantage in adding Asian coefficient to modifying the CKD-EPI equation. CysC GFR overestimated GFR in patients with CKD stages 4 and 5.
authors.affiliation: Department of Nephrology, Nanjing First Hospital Affiliated to Nanjing Medical University, Nanjing, China.
authors.forename: Xin
authors.initials: X
authors.lastname: DU
authors.forename: Lin
authors.initials: L
authors.lastname: Liu
authors.forename: Bo
authors.initials: B
authors.lastname: Hu
authors.forename: Feng
authors.initials: F
authors.lastname: Wang
authors.forename: Xin
authors.initials: X
authors.lastname: Wan
authors.forename: Linglin
authors.initials: L
authors.lastname: Jiang
authors.forename: Ruisheng
authors.initials: R
authors.lastname: Zhang
authors.forename: Changchun
authors.initials: C
authors.lastname: Cao
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D044466:Asian People; D015415:Biomarkers / Q000097:blood; D001830:Body Surface Area; D002681:China / Q000453:epidemiology; D002908:Chronic Disease; D003404:Creatinine / Q000097:blood; D055316:Cystatin C / Q000097:blood*; D005260:Female; D005919:Glomerular Filtration Rate; D006801:Humans; D007668:Kidney / Q000000981:diagnostic imaging / Q000378:metabolism* / Q000503:physiopathology; D007674:Kidney Diseases / Q000097:blood / Q000175:diagnosis* / Q000208:ethnology / Q000503:physiopathology; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011237:Predictive Value of Tests; D011379:Prognosis; D011877:Radionuclide Imaging; D019275:Radiopharmaceuticals; D012720:Severity of Illness Index; D016284:Technetium Tc 99m Pentetate
chemical_list: D015415:Biomarkers; C525030:CST3 protein, human; D055316:Cystatin C; D019275:Radiopharmaceuticals; D003404:Creatinine; D016284:Technetium Tc 99m Pentetate