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: "SI"

Expected Values: "SI", "CV", "umol/L", "mg/dL"

age

Age (years)

A numeric vector is expected.

weight

Weight (kg)

A numeric vector is expected if method = "CRCL"

sex

Gender

A character vector is expected.

Expected Values: "M", "F"

race

Race

A character vector is expected if method = "MDRD"

Expected Values: "BLACK OR AFRICAN AMERICAN" and others

method

Method

A character vector is expected.

Expected Values: "CRCL", "CKD-EPI", "MDRD"

## Value

A numeric vector of egfr values

## Details

Calculates an estimate of Glomerular Filtration Rate (eGFR)

CRCL Creatinine Clearance (Cockcroft-Gault)

For Creatinine in umol/L:

$$\frac{(140 - age) \times weight(kg) \times constant}{Serum\:Creatinine(\mu mol/L)}$$

$$Constant = 1.04\:for\:females, 1.23\:for\:males$$

For Creatinine in mg/dL:

$$\frac{(140 - age) \times weight(kg) \times (0.85\:if\:female)}{72 \times Serum\:Creatinine(mg/dL)}$$

units = mL/min

CKD-EPI Chronic Kidney Disease Epidemiology Collaboration formula

$$eGFR = 142 \times min(SCr/{\kappa}, 1)^{\alpha} \times max(SCr/{\kappa}, 1)^{-1.200} \times 0.9938^{Age} \times 1.012 [if\:female]$$

SCr = standardized serum creatinine in mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42)

$$\kappa$$ = 0.7 (females) or 0.9 (males) $$\alpha$$ = -0.241 (female) or -0.302 (male) units = mL/min/1.73 m2

MDRD Modification of Diet in Renal Disease formula

$$eGFR = 175 \times (SCr)^{-1.154} \times (age)^{-0.203} \times 0.742 [if\:female] \times 1.212 [if\:Black]$$

SCr = standardized serum creatinine in mg/dL (Note SCr(mg/dL) = Creat(umol/L) / 88.42)

units = mL/min/1.73 m2

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"
)
#> [1] 101.065

compute_egfr(
creat = 90, creatu = "umol/L", age = 53, sex = "M", race = "ASIAN", method = "MDRD"
)
#> [1] 76.58319

compute_egfr(
creat = 70, creatu = "umol/L", age = 52, sex = "F", race = "BLACK OR AFRICAN AMERICAN",
method = "MDRD"
)
#> [1] 92.40002

compute_egfr(
creat = 90, creatu = "umol/L", age = 53, sex = "M", method = "CKD-EPI"
)
#> [1] 88.10399

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"
),
)
#> # A tibble: 4 × 11
#>   STUDYID USUBJID    AGE SEX   RACE       WTBL CREATBL CREATBLU CRCL_CG EGFR_EPI
#>   <chr>   <chr>    <dbl> <chr> <chr>     <dbl>   <dbl> <chr>      <dbl>    <dbl>
#> 1 P01     P01-1001    55 M     WHITE      90.7    96.3 umol/L      98.5     80.2
#> 2 P01     P01-1002    52 F     BLACK OR…  68.5    70   umol/L      89.6     89.7
#> 3 P01     P01-1003    67 M     BLACK OR…  85      77   umol/L      99.1     94.5
#> 4 P01     P01-1004    76 F     ASIAN      60.7    65   umol/L      62.2     84.5
#> # ℹ 1 more variable: EGFR_MDRD <dbl>