Calculate Estimated Glomerular Filtration Rate for Children using the Schwartz Formula

Calculate Estimated Glomerular Filtration Rate (eGFR) for Pedatrics using the Schwartz formula

Usage

calculate_egfr_child(age, htcm, scr, sexf)

Arguments

age: Age (y). Numeric vector.
htcm: Height (cm). Numeric vector.
scr: Serum Creatinine (mg/dL). Numeric vector.
sexf: Sex. Numeric vector including values of 0 and/or 1. 0=Male; 1=Female.

Value

This function returns a numeric vector the same length as its inputs

Details

Formula to calculate EGFR_CHILD:
  Children (AGE [y] <= 16), where:
  EGFR = (k * HTCM [cm]) / SCR [mg/dL]
  k = 0.45 for AGE [y] <= 1 (full term)
  k = 0.55 for girls 1 < AGE [y] <= 16
  k = 0.55 for boys 1 < AGE [y] < 13
  k = 0.70 for boys 13 <= AGE [y] <= 16

References

Schwartz GJ, Furth SL. Glomerular filtration rate measurement and estimation in chronic kidney disease. Pediatr Nephrol. 2007;22(11):1839-48.

Examples

library(dplyr)

dmcognigen_cov %>% 
  mutate(EGFRSCHW = calculate_egfr_child(
    age = AGE, 
    htcm = HTCM, 
    scr = SCR, 
    sexf = SEXF
  ))
#> Formula to calculate EGFR_CHILD: 
#>   Children (AGE [y] <= 16), where:
#>   EGFR = (k * HTCM [cm]) / SCR [mg/dL]
#>   k = 0.45 for AGE [y] <= 1 (full term)
#>   k = 0.55 for girls 1 < AGE [y] <= 16
#>   k = 0.55 for boys 1 < AGE [y] < 13
#>   k = 0.70 for boys 13 <= AGE [y] <= 16
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `EGFRSCHW = calculate_egfr_child(age = AGE, htcm = HTCM, scr =
#>   SCR, sexf = SEXF)`.
#> Caused by warning:
#> ! Cases where AGE > 16 were detected. This formula is intended for children.
#> # A tibble: 254 × 53
#>    DOMAIN STUDYID USUBJID    ID RACEN RACEC  SEXF SEXFC  HTCM  WTKG   AST ASTULN
#>    <chr>  <chr>   <chr>   <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl>  <dbl>
#>  1 DM     CDISCP… 01-701… 10101     1 Whit…     1 Fema…  147.  54.4    40     34
#>  2 DM     CDISCP… 01-701… 10102     1 Whit…     0 Male   163.  80.3    21     36
#>  3 DM     CDISCP… 01-701… 10103     1 Whit…     0 Male   178.  99.3    24     36
#>  4 DM     CDISCP… 01-701… 10104     1 Whit…     0 Male   175.  88.4    20     36
#>  5 DM     CDISCP… 01-701… 10105     1 Whit…     1 Fema…  155.  62.6    23     34
#>  6 DM     CDISCP… 01-701… 10106     1 Whit…     1 Fema…  149.  67.1    25     34
#>  7 DM     CDISCP… 01-701… 10108     1 Whit…     0 Male   169.  78.0    19     36
#>  8 DM     CDISCP… 01-701… 10109     1 Whit…     1 Fema…  158.  59.9    28     34
#>  9 DM     CDISCP… 01-701… 10110     1 Whit…     0 Male   182.  78.9    26     36
#> 10 DM     CDISCP… 01-701… 10111     1 Whit…     0 Male   180.  71.2    15     36
#> # ℹ 244 more rows
#> # ℹ 41 more variables: SCR <dbl>, SCRULN <dbl>, TBIL <dbl>, TBILULN <dbl>,
#> #   ASTCAT <dbl>, BMI <dbl>, BSA <dbl>, IBW <dbl>, CRCL <dbl>, CRCLP <dbl>,
#> #   EGFR <dbl>, EGFRSCHW <dbl>, IBWCHILD <dbl>, LBM <dbl>, TBILCAT <dbl>,
#> #   RFCAT <dbl>, RFCATC <chr>, NCILIV <dbl>, NCILIVC <chr>, SUBJID <chr>,
#> #   RFSTDTC <chr>, RFENDTC <chr>, RFXSTDTC <chr>, RFXENDTC <chr>,
#> #   RFICDTC <chr>, RFPENDTC <chr>, DTHDTC <chr>, DTHFL <chr>, SITEID <chr>, …

# Below will also work if the dataset contains expected variables
dmcognigen_cov %>% 
  mutate(EGFRSCHW = calculate_egfr_child())
#> AGE variable found and used for the age argument.
#> HTCM variable found and used for the htcm argument.
#> SCR variable found and used for the scr argument.
#> SEXF variable found and used for the sexf argument.
#> Formula to calculate EGFR_CHILD: 
#>   Children (AGE [y] <= 16), where:
#>   EGFR = (k * HTCM [cm]) / SCR [mg/dL]
#>   k = 0.45 for AGE [y] <= 1 (full term)
#>   k = 0.55 for girls 1 < AGE [y] <= 16
#>   k = 0.55 for boys 1 < AGE [y] < 13
#>   k = 0.70 for boys 13 <= AGE [y] <= 16
#> Warning: There was 1 warning in `mutate()`.
#> ℹ In argument: `EGFRSCHW = calculate_egfr_child()`.
#> Caused by warning:
#> ! Cases where AGE > 16 were detected. This formula is intended for children.
#> # A tibble: 254 × 53
#>    DOMAIN STUDYID USUBJID    ID RACEN RACEC  SEXF SEXFC  HTCM  WTKG   AST ASTULN
#>    <chr>  <chr>   <chr>   <dbl> <dbl> <chr> <dbl> <chr> <dbl> <dbl> <dbl>  <dbl>
#>  1 DM     CDISCP… 01-701… 10101     1 Whit…     1 Fema…  147.  54.4    40     34
#>  2 DM     CDISCP… 01-701… 10102     1 Whit…     0 Male   163.  80.3    21     36
#>  3 DM     CDISCP… 01-701… 10103     1 Whit…     0 Male   178.  99.3    24     36
#>  4 DM     CDISCP… 01-701… 10104     1 Whit…     0 Male   175.  88.4    20     36
#>  5 DM     CDISCP… 01-701… 10105     1 Whit…     1 Fema…  155.  62.6    23     34
#>  6 DM     CDISCP… 01-701… 10106     1 Whit…     1 Fema…  149.  67.1    25     34
#>  7 DM     CDISCP… 01-701… 10108     1 Whit…     0 Male   169.  78.0    19     36
#>  8 DM     CDISCP… 01-701… 10109     1 Whit…     1 Fema…  158.  59.9    28     34
#>  9 DM     CDISCP… 01-701… 10110     1 Whit…     0 Male   182.  78.9    26     36
#> 10 DM     CDISCP… 01-701… 10111     1 Whit…     0 Male   180.  71.2    15     36
#> # ℹ 244 more rows
#> # ℹ 41 more variables: SCR <dbl>, SCRULN <dbl>, TBIL <dbl>, TBILULN <dbl>,
#> #   ASTCAT <dbl>, BMI <dbl>, BSA <dbl>, IBW <dbl>, CRCL <dbl>, CRCLP <dbl>,
#> #   EGFR <dbl>, EGFRSCHW <dbl>, IBWCHILD <dbl>, LBM <dbl>, TBILCAT <dbl>,
#> #   RFCAT <dbl>, RFCATC <chr>, NCILIV <dbl>, NCILIVC <chr>, SUBJID <chr>,
#> #   RFSTDTC <chr>, RFENDTC <chr>, RFXSTDTC <chr>, RFXENDTC <chr>,
#> #   RFICDTC <chr>, RFPENDTC <chr>, DTHDTC <chr>, DTHFL <chr>, SITEID <chr>, …