compute_egfr {admiral} | R Documentation |
Compute Estimated Glomerular Filtration Rate (eGFR) for Kidney Function
Description
Compute Kidney Function Tests:
Estimated Creatinine Clearance (CRCL) by Cockcroft-Gault equation
Estimated Glomerular Filtration Rate (eGFR) by CKD-EPI or MDRD equations
Usage
compute_egfr(creat, creatu = "SI", age, weight, sex, race = NULL, method)
Arguments
creat |
Creatinine A numeric vector is expected. |
creatu |
Creatinine Units A character vector is expected. Default: Expected Values: |
age |
Age (years) A numeric vector is expected. |
weight |
Weight (kg) A numeric vector is expected if |
sex |
Gender A character vector is expected. Expected Values: |
race |
Race A character vector is expected if Expected Values: |
method |
Method A character vector is expected. Expected Values: |
Details
Calculates an estimate of Glomerular Filtration Rate (eGFR)
CRCL Creatinine Clearance (Cockcroft-Gault)
For Creatinine in umol/L:
For Creatinine in mg/dL:
units = mL/min
CKD-EPI Chronic Kidney Disease Epidemiology Collaboration formula
SCr = standardized serum creatinine in mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42)
= 0.7 (females) or 0.9 (males)
= -0.241 (female) or -0.302 (male) units = mL/min/1.73 m2
MDRD Modification of Diet in Renal Disease formula
SCr = standardized serum creatinine in mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42)
units = mL/min/1.73 m2
Value
A numeric vector of egfr values
See Also
BDS-Findings Functions that returns a vector:
compute_bmi()
,
compute_bsa()
,
compute_framingham()
,
compute_map()
,
compute_qtc()
,
compute_qual_imputation()
,
compute_qual_imputation_dec()
,
compute_rr()
,
compute_scale()
Examples
compute_egfr(
creat = 90, creatu = "umol/L", age = 53, weight = 85, sex = "M", method = "CRCL"
)
compute_egfr(
creat = 90, creatu = "umol/L", age = 53, sex = "M", race = "ASIAN", method = "MDRD"
)
compute_egfr(
creat = 70, creatu = "umol/L", age = 52, sex = "F", race = "BLACK OR AFRICAN AMERICAN",
method = "MDRD"
)
compute_egfr(
creat = 90, creatu = "umol/L", age = 53, sex = "M", method = "CKD-EPI"
)
base <- tibble::tribble(
~STUDYID, ~USUBJID, ~AGE, ~SEX, ~RACE, ~WTBL, ~CREATBL, ~CREATBLU,
"P01", "P01-1001", 55, "M", "WHITE", 90.7, 96.3, "umol/L",
"P01", "P01-1002", 52, "F", "BLACK OR AFRICAN AMERICAN", 68.5, 70, "umol/L",
"P01", "P01-1003", 67, "M", "BLACK OR AFRICAN AMERICAN", 85.0, 77, "umol/L",
"P01", "P01-1004", 76, "F", "ASIAN", 60.7, 65, "umol/L",
)
base %>%
dplyr::mutate(
CRCL_CG = compute_egfr(
creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,
method = "CRCL"
),
EGFR_EPI = compute_egfr(
creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,
method = "CKD-EPI"
),
EGFR_MDRD = compute_egfr(
creat = CREATBL, creatu = CREATBLU, age = AGE, weight = WTBL, sex = SEX,
race = RACE, method = "MDRD"
),
)