nephro-package: Biostatistics utilities for nephrology
In nephro: Utilities for Nephrology
nephro-package R Documentation
Biostatistics utilities for nephrology
Description
This package contains a set of tools for the estimation of kidney function. Kidney function is assessed by means of the Glomerular Filtration Rate (GFR), which can be estimated using different biomarkers. The most commonly used ones are serum or plasma creatinine and cystatin C.
Included in the package are the following GFR estimating functions:
the Modification of Diet in Renal Disease (MDRD) study equations based on four (MDRD4) or six (MDRD6) parameters (Levey 1999; Levey 2006);
the CKD-Epi equations for serum creatinine with the race coefficient (CKDEpi.creat) and without the race coefficient (CKDEpi_RF.creat);
the CKD-Epi equation for cystatin C (CKDEpi.cys); the CKD-Epi equation for the combination of creatinine and cystatin C with (CKDEpi.creat.cys) and without (CKDEpi_RF.creat.cys) the race coefficient (Inker 2012; Inker 2021);
the three equations proposed by Stevens 2008 based on cystatin C only (Stevens.cys1), age- and sex-weighted cystatin C (Stevens.cys2), and a combination of cystatin C and creatinine (Stevens.creat.cys);
the classic Cockroft and Gault 1976 equation for creatinine clearance estimation (CG); the equation by Virga (2007) (Virga);
the race-free equations developed by the European Kidney Function Consortium (EKFC) including sex and age based on serum creatinine (EKFC.creat) (Pottel 2021) and based on serum cystatin C with (EKFC.cys) and without (EKFC_SF.cys) the sex coefficient (Pottel 2023);
the full age spectrum (FAS) equations using serum creatinine (FAS.creat) (Pottel 2016), cystatin C (FAS.cys), and their combination (FAS.creat.cys) (Pottel 2017);
the Schwartz bedside formula (Schwartz.Bedside) (Schwartz 2009).
A comparative description of several functions included in the initial version of the package can be found in Pattaro (2013). Extensive literature does exist that compares the methods described.
Details
Package: nephroext
Type: Package
Version: 1.4
Date: 2023-10-10
License: GPLv3
Author(s)
Cristian Pattaro <cristian.pattaro@eurac.edu>, Ryosuke Fujii <ryosuke.fujii@eurac.edu>, Janina Herold <janina.herold@klinik.uni-regensburg.de>
Acknowledgements: Max Plischke, Joao Sabino, Xiao Jiang for bug reporting; Alexander Tolios, Thomas Winkler for input on the software package; Andrew Srisuwananukorn for contributing scripts; the 'nephro' user community for continuous feedback.
References
Citing this package:
- Pattaro C, Riegler P, Stifter G, Modenese M, Minelli C, Pramstaller PP. Estimating the glomerular filtration rate in the general population using different equations: effects on classification and association. Nephron Clin Pract 2013; 123(1-2):102-11.
Formulas:
- Cockroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976; 16: 31-41.
- Inker LA, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012; 367: 20-9.
- Inker LA, et al. New Creatinine- and Cystatin C-based Equations to Estimate GFR without Race. N Engl J Med 2021; 385: 1737-1749.
- Levey AS, et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999; 130(6): 461-70.
- Levey AS, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006; 145: 247-54.
- Stevens LA, et al. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3,418 individuals with CKD. Am J Kidney Dis 2008; 51: 395-406.
- Virga G, et al. A new equation for estimating renal function using age, body weight and serum creatinine. Nephron Clin Pract 2007; 105: c43-53.
- Pottel H, et al. Development and Validation of a Modified Full Age Spectrum Creatinine-Based Equation to Estimate Glomerular Filtration Rate: A Cross-sectional Analysis of Pooled Data. Ann Intern Med 2021; 174: 183-191.
- Pottel H, et al. Cystatin C-Based Equation to Estimate GFR without the Inclusion of Race and Sex. N Engl J Med 2023; 388: 333-343.
- Pottel H, et al. An estimated glomerular filtration rate equation for the full age spectrum. Nephrol Dial Transplant 2016; 31:798-806.
- Pottel H, et al. Estimating glomerular filtration rate for the full age spectrum from serum creatinine and cystatin C Nephrol Dial Transplant 2017; 32: 497-507.
- Schwartz GJ, et al. New equations to estimate GFR in children with CKD. J Am Soc Nephrol 2009; 20:629-637.
On IDMS calibration:
- Levey AS, et al. Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem 2007; 53:766-72.
- Matsushita K, et al. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. J Am Med Assoc 2012; 307:1941-51.
- Skali H, et al. Prognostic assessment of estimated glomerular filtration rate by the new Chronic Kidney Disease Epidemiology Collaboration equation in comparison with the Modification of Diet in Renal Disease Study equation. Am Heart J 2011; 162:548-54.
Examples
# Comparison between different equations
creat <- c(0.8, 0.9, 1.0, 1.1, 1.2, 1.3)
cyst <- c(1.1, 0.95, 1.1, 1.0, 1.3, 1.2)
sex <- c(1, 1, 1, 0, 0, 0)
age <- c(60, 65, 43, 82, 71, 55)
ethn <- round(runif(6))
wt <- c(70, 80, 60, 55, 87, 71)
eGFR <- data.frame(creat, cyst)
eGFR$MDRD4 <- MDRD4(creat, sex, age, ethn, 'IDMS')
eGFR$CKDEpi.creat <- CKDEpi.creat(creat, sex, age, ethn)
eGFR$CKDEpi_RF.creat <- CKDEpi_RF.creat(creat, sex, age)
eGFR$CKDEpi.cys <- CKDEpi.cys(cyst, sex, age)
eGFR$CKDEpi.creat.cys <- CKDEpi.creat.cys(creat, cyst, sex, age, ethn)
eGFR$CKDEpi_RF.creat.cys <- CKDEpi_RF.creat.cys(creat, cyst, sex, age)
eGFR$Stevens.cys1 <- Stevens.cys1(cyst)
eGFR$Stevens.cys2 <- Stevens.cys2(cyst, sex, age, ethn)
eGFR$Stevens.creat.cys <- Stevens.creat.cys(creat, cyst, sex, age, ethn)
eGFR$cg <- CG(creat, sex, age, wt)
eGFR$virga <- Virga(creat, sex, age, wt)
pairs(eGFR[,3:13])
# For use with non-IDMS calibrated creatinine
# several authors (see references) suggested
# a 5% creatinine adjustment
creat <- c(0.8, 0.9, 1.0, 1.1, 1.2, 1.3)
sex <- c(1, 1, 1, 0, 0, 0)
age <- c(60, 65, 43, 82, 71, 55)
ethn <- round(runif(6))
gfr <- CKDEpi.creat(0.95*creat, sex, age, ethn)
nephro documentation built on Oct. 17, 2023, 1:09 a.m.
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| nephro-package | R Documentation |
Biostatistics utilities for nephrology
Description
This package contains a set of tools for the estimation of kidney function. Kidney function is assessed by means of the Glomerular Filtration Rate (GFR), which can be estimated using different biomarkers. The most commonly used ones are serum or plasma creatinine and cystatin C.
Included in the package are the following GFR estimating functions:
the Modification of Diet in Renal Disease (MDRD) study equations based on four (MDRD4) or six (MDRD6) parameters (Levey 1999; Levey 2006);
the CKD-Epi equations for serum creatinine with the race coefficient (CKDEpi.creat) and without the race coefficient (CKDEpi_RF.creat);
the CKD-Epi equation for cystatin C (CKDEpi.cys); the CKD-Epi equation for the combination of creatinine and cystatin C with (CKDEpi.creat.cys) and without (CKDEpi_RF.creat.cys) the race coefficient (Inker 2012; Inker 2021);
the three equations proposed by Stevens 2008 based on cystatin C only (Stevens.cys1), age- and sex-weighted cystatin C (Stevens.cys2), and a combination of cystatin C and creatinine (Stevens.creat.cys);
the classic Cockroft and Gault 1976 equation for creatinine clearance estimation (CG); the equation by Virga (2007) (Virga);
the race-free equations developed by the European Kidney Function Consortium (EKFC) including sex and age based on serum creatinine (EKFC.creat) (Pottel 2021) and based on serum cystatin C with (EKFC.cys) and without (EKFC_SF.cys) the sex coefficient (Pottel 2023);
the full age spectrum (FAS) equations using serum creatinine (FAS.creat) (Pottel 2016), cystatin C (FAS.cys), and their combination (FAS.creat.cys) (Pottel 2017);
the Schwartz bedside formula (Schwartz.Bedside) (Schwartz 2009).
A comparative description of several functions included in the initial version of the package can be found in Pattaro (2013). Extensive literature does exist that compares the methods described.
Details
| Package: | nephroext |
| Type: | Package |
| Version: | 1.4 |
| Date: | 2023-10-10 |
| License: | GPLv3 |
Author(s)
Cristian Pattaro <cristian.pattaro@eurac.edu>, Ryosuke Fujii <ryosuke.fujii@eurac.edu>, Janina Herold <janina.herold@klinik.uni-regensburg.de>
Acknowledgements: Max Plischke, Joao Sabino, Xiao Jiang for bug reporting; Alexander Tolios, Thomas Winkler for input on the software package; Andrew Srisuwananukorn for contributing scripts; the 'nephro' user community for continuous feedback.
References
Citing this package:
- Pattaro C, Riegler P, Stifter G, Modenese M, Minelli C, Pramstaller PP. Estimating the glomerular filtration rate in the general population using different equations: effects on classification and association. Nephron Clin Pract 2013; 123(1-2):102-11.
Formulas:
- Cockroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976; 16: 31-41.
- Inker LA, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012; 367: 20-9.
- Inker LA, et al. New Creatinine- and Cystatin C-based Equations to Estimate GFR without Race. N Engl J Med 2021; 385: 1737-1749.
- Levey AS, et al. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999; 130(6): 461-70.
- Levey AS, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006; 145: 247-54.
- Stevens LA, et al. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3,418 individuals with CKD. Am J Kidney Dis 2008; 51: 395-406.
- Virga G, et al. A new equation for estimating renal function using age, body weight and serum creatinine. Nephron Clin Pract 2007; 105: c43-53.
- Pottel H, et al. Development and Validation of a Modified Full Age Spectrum Creatinine-Based Equation to Estimate Glomerular Filtration Rate: A Cross-sectional Analysis of Pooled Data. Ann Intern Med 2021; 174: 183-191.
- Pottel H, et al. Cystatin C-Based Equation to Estimate GFR without the Inclusion of Race and Sex. N Engl J Med 2023; 388: 333-343.
- Pottel H, et al. An estimated glomerular filtration rate equation for the full age spectrum. Nephrol Dial Transplant 2016; 31:798-806.
- Pottel H, et al. Estimating glomerular filtration rate for the full age spectrum from serum creatinine and cystatin C Nephrol Dial Transplant 2017; 32: 497-507.
- Schwartz GJ, et al. New equations to estimate GFR in children with CKD. J Am Soc Nephrol 2009; 20:629-637.
On IDMS calibration:
- Levey AS, et al. Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem 2007; 53:766-72.
- Matsushita K, et al. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. J Am Med Assoc 2012; 307:1941-51.
- Skali H, et al. Prognostic assessment of estimated glomerular filtration rate by the new Chronic Kidney Disease Epidemiology Collaboration equation in comparison with the Modification of Diet in Renal Disease Study equation. Am Heart J 2011; 162:548-54.
Examples
# Comparison between different equations
creat <- c(0.8, 0.9, 1.0, 1.1, 1.2, 1.3)
cyst <- c(1.1, 0.95, 1.1, 1.0, 1.3, 1.2)
sex <- c(1, 1, 1, 0, 0, 0)
age <- c(60, 65, 43, 82, 71, 55)
ethn <- round(runif(6))
wt <- c(70, 80, 60, 55, 87, 71)
eGFR <- data.frame(creat, cyst)
eGFR$MDRD4 <- MDRD4(creat, sex, age, ethn, 'IDMS')
eGFR$CKDEpi.creat <- CKDEpi.creat(creat, sex, age, ethn)
eGFR$CKDEpi_RF.creat <- CKDEpi_RF.creat(creat, sex, age)
eGFR$CKDEpi.cys <- CKDEpi.cys(cyst, sex, age)
eGFR$CKDEpi.creat.cys <- CKDEpi.creat.cys(creat, cyst, sex, age, ethn)
eGFR$CKDEpi_RF.creat.cys <- CKDEpi_RF.creat.cys(creat, cyst, sex, age)
eGFR$Stevens.cys1 <- Stevens.cys1(cyst)
eGFR$Stevens.cys2 <- Stevens.cys2(cyst, sex, age, ethn)
eGFR$Stevens.creat.cys <- Stevens.creat.cys(creat, cyst, sex, age, ethn)
eGFR$cg <- CG(creat, sex, age, wt)
eGFR$virga <- Virga(creat, sex, age, wt)
pairs(eGFR[,3:13])
# For use with non-IDMS calibrated creatinine
# several authors (see references) suggested
# a 5% creatinine adjustment
creat <- c(0.8, 0.9, 1.0, 1.1, 1.2, 1.3)
sex <- c(1, 1, 1, 0, 0, 0)
age <- c(60, 65, 43, 82, 71, 55)
ethn <- round(runif(6))
gfr <- CKDEpi.creat(0.95*creat, sex, age, ethn)
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