Using KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease Volume 3 | Issue 1 | January 2013
eGFR(...)
# S3 method for data.frame
eGFR(
.data,
SCr = NULL,
SCysC = NULL,
Age = NULL,
height = NULL,
BUN = NULL,
male = NULL,
black = NULL,
pediatric = NULL,
...
)
# S3 method for units
eGFR(
SCr = NULL,
SCysC = NULL,
Age = NULL,
height = NULL,
BUN = NULL,
male = NULL,
black = NULL,
pediatric = NULL,
...
)
# S3 method for numeric
eGFR(
SCr = NULL,
SCysC = NULL,
Age = NULL,
height = NULL,
BUN = NULL,
male = NULL,
black = NULL,
pediatric = NULL,
...
)Further optional arguments
(data.frame) A data.frame, optional
Serum creatinine
column name, or vector of units or numeric if .data is not provided
Serum Cystatin C
column name, or vector of units or numeric if .data is not provided
Age of patient
column name, or vector of units or numeric if .data is not provided
Height of patient
column name, or vector of units or numeric if .data is not provided
Blood urea nitrogen
column name, or vector of units or numeric if .data is not provided
Male or not
column name, or vector of logical (TRUE/FALSE) if .data is not provided
Black race or not
column name, or vector of logical (TRUE/FALSE) if .data is not provided
(logical) Pediatric or not
column name, or vector of logical (TRUE/FALSE) if .data is not provided
(units) Estimated glomerular filtration rate (eGFR) of the same type provided (numeric or units in ml/min/1.73m2)
Automatic selection of equation to estimation the Glomerular Filtration Rate (eGFR), based on input data
eGFR_adult_SCr(): 2009 CKD-EPI creatinine equation
eGFR_adult_SCysC(): 2012 CKD-EPI cystatin C equation
eGFR_adult_SCr_SCysC(): 2012 CKD-EPI creatinine-cystatin C equation
eGFR_child_SCr(): Pediatric creatinine-based equation
eGFR_child_SCr_BUN(): Pediatric creatinine-BUN equation
eGFR_child_SCysC(): Pediatric cystatin C-based equation
See https://kdigo.org/guidelines/ckd-evaluation-and-management/ for more details
eGFR(eGFR_pt_data,
SCr = "SCr_", SCysC = "SCysC_",
Age = "Age_", height = "height_", BUN = "BUN_",
male = "male_", black = "black_", pediatric = "pediatric_"
)
#> Units: [mL/1.73m2/min]
#> [1] 139.32466 161.68446 166.81886 150.52336 155.33226 171.35616 139.32466
#> [8] 66.77365 96.41798 150.52336 64.15027 99.04045 49.63420 161.68446
#> [15] 97.06854 53.62373 155.33226 99.70870 49.63420 66.77365 56.10368
#> [22] 53.62373 64.15027 57.62964 155.99874 173.48118 178.86404 168.53768
#> [29] 166.66552 183.72895 155.99874 71.64555 103.37985 168.53768 68.83077
#> [36] 106.19167 66.06766 173.48118 116.50660 71.37808 166.66552 119.67546
#> [43] 66.06766 71.64555 67.33849 71.37808 68.83077 69.17003 99.12000
#> [50] 148.21219 165.89761
eGFR_pt_data %>%
dplyr::mutate(eGFR = eGFR(
SCr = SCr_, SCysC = SCysC_,
Age = Age_, height = height_, BUN = BUN_,
male = male_, black = black_, pediatric = pediatric_
))
#> # A tibble: 51 × 11
#> SCr_ SCysC_ Age_ male_ black_ height_ BUN_ eGFR_calc_type_ eGFR_
#> [mg/dl] [mg/L] [years] <lgl> <lgl> [m] [mg/dl] <chr> [mL/…
#> 1 0.5 NA 20 FALSE FALSE NA NA eGFR_adult_SCr 139.
#> 2 NA 0.4 20 FALSE FALSE NA NA eGFR_adult_SCysC 162.
#> 3 0.5 0.4 20 FALSE FALSE NA NA eGFR_adult_SCr_SCy… 167.
#> 4 0.5 NA 30 FALSE TRUE NA NA eGFR_adult_SCr 150.
#> 5 NA 0.4 30 FALSE TRUE NA NA eGFR_adult_SCysC 155.
#> 6 0.5 0.4 30 FALSE TRUE NA NA eGFR_adult_SCr_SCy… 171.
#> 7 0.5 NA 20 FALSE FALSE NA NA eGFR_adult_SCr 139.
#> 8 NA 1.2 20 FALSE FALSE NA NA eGFR_adult_SCysC 66.8
#> 9 0.5 1.2 20 FALSE FALSE NA NA eGFR_adult_SCr_SCy… 96.4
#> 10 0.5 NA 30 FALSE TRUE NA NA eGFR_adult_SCr 150.
#> # ℹ 41 more rows
#> # ℹ 2 more variables: pediatric_ <lgl>, eGFR [mL/1.73m2/min]