BACKGROUND: Serum creatinine (S-Cr)-based prediction equations are commonly used for estimating glomerular filtration rate (GFR). However, S-Cr concentration is also affected by other factors such as tubular secretion, muscle mass, diet, gender and age. Serum cystatin C (S-Cys C)-based prediction equations have been proposed as an improved potential alternative as S-Cys C levels are not influenced by many of the factors that affect creatinine concentration other than GFR. This may be of great benefit to patients with low muscle mass such as those infected with human immunodeficiency virus who are at increased risk for the development of renal impairment. The aim of this study was to develop and evaluate a S-Cys C-based prediction equation for different stages of renal disease in black South Africans. METHODS: One hundred patients with varying degrees of renal function were enrolled in the study. The plasma clearance of (51)Cr-EDTA, a gold standard method, was used to measure GFR (mGFR). In addition, serum was analysed for S-Cr and S-Cys C on each participant. This dataset was split into a development dataset (n = 50) and a test dataset (n = 50). The development dataset was used to formulate a S-Cys C- and S-Cr-based prediction equation using multiple linear regression analysis. These equations together with the four-variable MDRD and CKD-EPI equation were then tested on the test dataset. RESULTS: In the test dataset, accuracy within 15% of measured GFR was 68% for the S-Cys C equation and 48% for the S-Cr equation. Root mean square error for S-Cr eGFR was 10.7 mL/min/1.73 m(2) for those patients with mGFR < 60 mL/min/1.73 m(2) and 25.5 mL/min/1.73 m(2) for those patients with mGFR > 60 mL/min/1.73 m(2). Root mean square error for S-Cys C eGFR was 10.2 mL/min/1.73 m(2) for those patients with mGFR < 60 mL/min/1.73 m(2) and 11.9 mL/min/1.73 m(2) for those patients with mGFR > 60 mL/min/1.73 m(2). CONCLUSIONS: In this study, S-Cys C-based prediction equations appear to be more precise than those of S-Cr for those patients with mGFR > 60 mL/min/1.73 m(2) and may therefore be of benefit in the earlier detection of renal impairment.
BACKGROUND: Serum creatinine (S-Cr)-based prediction equations are commonly used for estimating glomerular filtration rate (GFR). However, S-Cr concentration is also affected by other factors such as tubular secretion, muscle mass, diet, gender and age. Serum cystatin C (S-Cys C)-based prediction equations have been proposed as an improved potential alternative as S-Cys C levels are not influenced by many of the factors that affect creatinine concentration other than GFR. This may be of great benefit to patients with low muscle mass such as those infected with human immunodeficiency virus who are at increased risk for the development of renal impairment. The aim of this study was to develop and evaluate a S-Cys C-based prediction equation for different stages of renal disease in black South Africans. METHODS: One hundred patients with varying degrees of renal function were enrolled in the study. The plasma clearance of (51)Cr-EDTA, a gold standard method, was used to measure GFR (mGFR). In addition, serum was analysed for S-Cr and S-Cys C on each participant. This dataset was split into a development dataset (n = 50) and a test dataset (n = 50). The development dataset was used to formulate a S-Cys C- and S-Cr-based prediction equation using multiple linear regression analysis. These equations together with the four-variable MDRD and CKD-EPI equation were then tested on the test dataset. RESULTS: In the test dataset, accuracy within 15% of measured GFR was 68% for the S-Cys C equation and 48% for the S-Cr equation. Root mean square error for S-Cr eGFR was 10.7 mL/min/1.73 m(2) for those patients with mGFR < 60 mL/min/1.73 m(2) and 25.5 mL/min/1.73 m(2) for those patients with mGFR > 60 mL/min/1.73 m(2). Root mean square error for S-Cys C eGFR was 10.2 mL/min/1.73 m(2) for those patients with mGFR < 60 mL/min/1.73 m(2) and 11.9 mL/min/1.73 m(2) for those patients with mGFR > 60 mL/min/1.73 m(2). CONCLUSIONS: In this study, S-Cys C-based prediction equations appear to be more precise than those of S-Cr for those patients with mGFR > 60 mL/min/1.73 m(2) and may therefore be of benefit in the earlier detection of renal impairment.
Authors: M D Blaufox; M Aurell; B Bubeck; E Fommei; A Piepsz; C Russell; A Taylor; H S Thomsen; D Volterrani Journal: J Nucl Med Date: 1996-11 Impact factor: 10.057
Authors: Lesley A Stevens; Josef Coresh; Christopher H Schmid; Harold I Feldman; Marc Froissart; John Kusek; Jerome Rossert; Frederick Van Lente; Robert D Bruce; Yaping Lucy Zhang; Tom Greene; Andrew S Levey Journal: Am J Kidney Dis Date: 2008-03 Impact factor: 8.860
Authors: Andrew S Levey; Lesley A Stevens; Christopher H Schmid; Yaping Lucy Zhang; Alejandro F Castro; Harold I Feldman; John W Kusek; Paul Eggers; Frederick Van Lente; Tom Greene; Josef Coresh Journal: Ann Intern Med Date: 2009-05-05 Impact factor: 25.391
Authors: Kenneth K Mugwanya; Christina Wyatt; Connie Celum; Deborah Donnell; James Kiarie; Allan Ronald; Jared M Baeten Journal: J Acquir Immune Defic Syndr Date: 2016-04-01 Impact factor: 3.731
Authors: Kenneth K Mugwanya; Christina Wyatt; Connie Celum; Deborah Donnell; Nelly R Mugo; Jordan Tappero; James Kiarie; Allan Ronald; Jared M Baeten Journal: JAMA Intern Med Date: 2015-02 Impact factor: 21.873
Authors: Lesley A Inker; Christina Wyatt; Rebecca Creamer; James Hellinger; Matthew Hotta; Maia Leppo; Andrew S Levey; Aghogho Okparavero; Hiba Graham; Karen Savage; Christopher H Schmid; Hocine Tighiouart; Fran Wallach; Zipporah Krishnasami Journal: J Acquir Immune Defic Syndr Date: 2012-11-01 Impact factor: 3.731
Authors: Willemijn L Eppenga; Matthijs van Luin; Clemens Richter; Hieronymus J Derijks; Peter A G M De Smet; Michel Wensing Journal: J Nephrol Date: 2013-12-20 Impact factor: 3.902
Authors: Willemijn L Eppenga; Cornelis Kramers; Hieronymus J Derijks; Michel Wensing; Jack F M Wetzels; Peter A G M De Smet Journal: PLoS One Date: 2015-03-05 Impact factor: 3.240
Authors: Patrick H Dessein; Hon-Chun Hsu; Linda Tsang; Aletta M E Millen; Angela J Woodiwiss; Gavin R Norton; Ahmed Solomon; Miguel A Gonzalez-Gay Journal: PLoS One Date: 2015-03-25 Impact factor: 3.240