title: [Comparison of eight equations for estimating glomerular filtration rate in patients with cardiovascular diseases].
abstract: OBJECTIVE
  To assess the diagnostic value of 8 equations using different variables for determining the estimated glomerular filtration rate (eGFR) in patients with cardiovascular diseases.
  
  
  METHODS
  GFR was estimated in 208 patients with cardiovascular diseases by (99m)Tc-DTPA dynamic renal imaging, and the eGFR was derived from 8 equations using different variables.
  
  
  RESULTS
  In patients with chronic kidney disease (CKD) stages 1-3, the eGFR calculated suing serum creatinine (SCr)-based equation was better correlated to GFR estimated by (99m)Tc-DTPA renal imaging than that derived from cystatin C (Cys C)-based equations, whereas in patients with CKD stages 4 and 5, the estimates by the latter equation showed a better correlation to GFR. Compared with (99m)Tc-DTPA renal imaging, MDRD-based equation and simple MDRD equation resulted in a higher eGFR in patients with CKD stages 4 and 5, the Rule equation had a lower eGFR in CKD stages 1 and 2, the Macisaac equation yielded a higher eGFR in CKD stages 2-5, and the Tan equation showed a higher eGFR in CKD stages 2 and 3. In patients with mild renal dysfunction, the Scr-based equation had a higher AUC(ROC) than Cys C-based equation, which was reversed in patients with severe renal dysfunction; the AUC(ROC) of the two equations were comparable in patients with moderate renal dysfunction. Compared with (99m)Tc-DTPA renal imaging, the modified MDRD equation and Arnal-Dade equation showed no significant difference in the eGFR in patients with CKD stages 1-5.
  
  
  CONCLUSIONS
  Modified MDRD equation (or simple MDRD equation) and Arnal-Dade equation are superior to other calculation methods for estimating the GFR in Chinese patients with cardiovascular disease.
authors.affiliation: Department of Cardiology, Nanfang Hospital, Guangzhou 510515, China. suliang20030824@yahoo.com.cn
authors.forename: Liang
authors.initials: L
authors.lastname: Su
authors.forename: Shu-chang
authors.initials: SC
authors.lastname: Bai
authors.forename: Bundhoo
authors.initials: B
authors.lastname: Kaviraj
authors.forename: Zheng
authors.initials: Z
authors.lastname: Huang
authors.forename: Yu-qing
authors.initials: YQ
authors.lastname: Hou
authors.forename: Hao
authors.initials: H
authors.lastname: Ren
authors.forename: Ding-li
authors.initials: DL
authors.lastname: Xu
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002318:Cardiovascular Diseases / Q000503:physiopathology*; D002648:Child; D002908:Chronic Disease; D003404:Creatinine / Q000097:blood; D055316:Cystatin C / Q000097:blood; D005260:Female; D005919:Glomerular Filtration Rate / Q000502:physiology*; D006801:Humans; D007674:Kidney Diseases / Q000503:physiopathology*; D008297:Male; D008875:Middle Aged; D055815:Young Adult
chemical_list: C525030:CST3 protein, human; D055316:Cystatin C; D003404:Creatinine