eGFR 2012 CKD-EPI creatinine-cystatin C equation — eGFR_adult_SCr

Using KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease Volume 3 | Issue 1 | January 2013

Usage

eGFR_adult_SCr_SCysC(...)

# S3 method for class 'data.frame'
eGFR_adult_SCr_SCysC(.data, SCr, SCysC, Age, male, black, ...)

# S3 method for class 'units'
eGFR_adult_SCr_SCysC(SCr, SCysC, Age, male, black, ...)

# S3 method for class 'numeric'
eGFR_adult_SCr_SCysC(SCr, SCysC, Age, male, black, ...)

Arguments

...: Further optional arguments
.data: (data.frame) A data.frame, optional
SCr: Serum creatinine column name, or vector of units or numeric if .data is not provided
SCysC: Serum Cystatin C column name, or vector of units or numeric if .data is not provided
Age: Age of patient column name, or vector of units or numeric if .data is not provided
male: Male or not column name, or vector of logical (TRUE/FALSE) if .data is not provided
black: Black race or not column name, or vector of logical (TRUE/FALSE) if .data is not provided

Value

Estimated GFR of the same type provided (numeric or units)

Details

See https://kdigo.org/guidelines/ckd-evaluation-and-management/ for more details

Examples

eGFR_adult_SCr_SCysC(eGFR_pt_data,
  SCr = "SCr_", SCysC = "SCysC_",
  Age = "Age_", male = "male_", black = "black_"
)
#> Units: [mL/(min*1.73m^2)]
#>  [1]        NA        NA 166.81886        NA        NA 171.35616        NA
#>  [8]        NA  96.41798        NA        NA  99.04045        NA        NA
#> [15]  97.06854        NA        NA  99.70870        NA        NA  56.10368
#> [22]        NA        NA  57.62964        NA        NA 178.86404        NA
#> [29]        NA 183.72895        NA        NA 103.37985        NA        NA
#> [36] 106.19167        NA        NA 116.50660        NA        NA 119.67546
#> [43]        NA        NA  67.33849        NA        NA  69.17003        NA
#> [50]        NA        NA

eGFR_pt_data %>%
  dplyr::mutate(eGFR = eGFR_adult_SCr_SCysC(
    SCr = SCr_, SCysC = SCysC_,
    Age = Age_, male = male_, black = black_
  ))
#> # 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/(min*1.73m^2)]