README.md
In alwinw/epocakir: Clinical Coding of Patients with Kidney Disease
epocakir
/ˈiːpɒk ə keɪ aɪ ɑː/
Clinical Coding of Patients with Kidney Disease
The epocakir package makes clinical coding of patients with kidney
disease using clinical practice guidelines easy. The guidelines used are
the evidence-based KDIGO guidelines.
This package covers acute kidney injury (AKI), anemia, and chronic
kidney disease (CKD):
-
aki_staging(): Classification of AKI staging (aki_stages) with
automatic selection of:
aki_bCr(): AKI based on baseline creatinineaki_SCr(): AKI based on changes in serum creatinineaki_UO(): AKI based on urine output
-
anemia(): Classification of anemia
-
Classification of albuminuria (Albuminuria_stages)
Albuminuria_staging_ACR(): Albuminuria based on Albumin
excretion rateAlbuminuria_staging_AER(): Albuminuria based on
Albumin-to-creatinine ratio
-
eGFR(): Estimation of glomerular filtration rate with automatic
selection of:
eGFR_adult_SCr(): eGFR based on the 2009 CKD-EPI creatinine
equationeGFR_adult_SCysC(): eGFR based on the 2012 CKD-EPI cystatin C
equationeGFR_adult_SCr_SCysC(): eGFR based on the 2012 CKD-EPI
creatinine-cystatin C equationeGFR_child_SCr(): eGFR based on the pediatric creatinine-based
equationeGFR_child_SCr_BUN(): eGFR based on the pediatric
creatinine-BUN equationeGFR_child_SCysC(): eGFR based on the pediatric cystatin
C-based equation
-
GFR_staging(): Staging of GFR (GFR_stages)
-
Multiple utility functions including:
conversion_factors: Conversion factors used throughout the
KDIGO guidelinesas_metric(): Conversion of a measured value into metric unitsdob2age(): Calculation of age from a date of birthbinary2factor(): Conversion of binary data into factors based
on a column namecombine_date_time_cols(): Combining separate date and time
columns into a single date and time columncombn_changes: Generating changes between measurements
Installation
You can install the released version from
CRAN with:
install.packages("epocakir")
You can install the development version from
GitHub with:
# install.packages("remotes")
remotes::install_github("alwinw/epocakir")
Getting Started
library(epocakir)
library(dplyr)
library(units)
Often clinical data must be cleansed and tidied before analysis can
begin. To assist in this, several utility functions have been included.
To explore these, consider a sample clinical dataset clinical_obvs:
glimpse(clinical_obvs)
#> Rows: 3
#> Columns: 9
#> $ `Patient Number` <chr> "p10001", "p10002", "p10003"
#> $ `Admission Date` <chr> "2020-03-05", "2020-03-06", "2020-03-17"
#> $ `Admission Time` <chr> "14:01:00", "09:10:00", "12:48:00"
#> $ Discharge_date <chr> "2020-03-10", "2020-03-16", "2020-03-18"
#> $ Discharge_time <chr> "16:34:00", "18:51:00", "09:12:00"
#> $ `Date of Birth` <chr> "1956-01-09", "1997-12-04", "1973-05-28"
#> $ Male <lgl> TRUE, FALSE, TRUE
#> $ Height <dbl> 182, 161, 168
#> $ Surgery <lgl> FALSE, FALSE, TRUE
tidy_obvs <- clinical_obvs %>%
combine_date_time_cols() %>%
mutate(
Age = dob2age(`Date of Birth`),
Height = as_metric(height = set_units(as.numeric(Height), "cm"))
) %>%
binary2factor(Male, Surgery)
glimpse(tidy_obvs)
#> Rows: 3
#> Columns: 8
#> $ `Patient Number` <chr> "p10001", "p10002", "p10003"
#> $ `Admission DateTime` <dttm> 2020-03-05 14:01:00, 2020-03-06 09:10:00, 2020-03…
#> $ Discharge_DateTime <dttm> 2020-03-10 16:34:00, 2020-03-16 18:51:00, 2020-0…
#> $ `Date of Birth` <chr> "1956-01-09", "1997-12-04", "1973-05-28"
#> $ Male <ord> Male, Not_Male, Male
#> $ Height [m] 1.82 [m], 1.61 [m], 1.68 [m]
#> $ Surgery <ord> Not_Surgery, Not_Surgery, Surgery
#> $ Age <Duration> 2092780800s (~66.32 years), 770428800s (~24.41 y…
Make sure to use set_units() from the units package to convert all
measurements into unit objects for automatic unit conversion in
epocakir.
Examples
It is possible to use aki_staging() to automatically classify the
presence and staging of AKI. If a particular method is required, it is
possible to classify AKI using aki_bCr(), aki_SCr() or aki_UO().
head(aki_pt_data)
#> # A tibble: 6 × 7
#> SCr_ bCr_ pt_id_ dttm_ UO_ aki_staging_type aki_
#> [mg/dl] [mg/dl] <chr> <dttm> [ml/kg] <chr> <ord>
#> 1 2 1.5 <NA> NA NA aki_bCr No AKI
#> 2 2.5 1.5 <NA> NA NA aki_bCr AKI Stage 1
#> 3 3 1.5 <NA> NA NA aki_bCr AKI Stage 2
#> 4 3.5 1.5 <NA> NA NA aki_bCr AKI Stage 2
#> 5 4 1.5 <NA> NA NA aki_bCr AKI Stage 3
#> 6 4.5 1.5 <NA> NA NA aki_bCr AKI Stage 3
aki_staging(aki_pt_data,
SCr = "SCr_", bCr = "bCr_", UO = "UO_",
dttm = "dttm_", pt_id = "pt_id_"
)
#> [1] No AKI AKI Stage 1 AKI Stage 2 AKI Stage 2 AKI Stage 3 AKI Stage 3
#> [7] No AKI No AKI AKI Stage 1 No AKI No AKI AKI Stage 1
#> [13] No AKI No AKI No AKI AKI Stage 1 No AKI AKI Stage 2
#> [19] AKI Stage 3 AKI Stage 1 AKI Stage 3 AKI Stage 2 No AKI AKI Stage 1
#> [25] AKI Stage 3 AKI Stage 3 No AKI
#> Levels: No AKI < AKI Stage 1 < AKI Stage 2 < AKI Stage 3
aki_pt_data %>%
mutate(aki = aki_staging(
SCr = SCr_, bCr = bCr_, UO = UO_,
dttm = dttm_, pt_id = pt_id_
)) %>%
select(pt_id_, SCr_:dttm_, aki)
#> # A tibble: 27 × 5
#> pt_id_ SCr_ bCr_ dttm_ aki
#> <chr> [mg/dl] [mg/dl] <dttm> <ord>
#> 1 <NA> 2 1.5 NA No AKI
#> 2 <NA> 2.5 1.5 NA AKI Stage 1
#> 3 <NA> 3 1.5 NA AKI Stage 2
#> 4 <NA> 3.5 1.5 NA AKI Stage 2
#> 5 <NA> 4 1.5 NA AKI Stage 3
#> 6 <NA> 4.5 1.5 NA AKI Stage 3
#> 7 pt1 3.4 NA 2020-10-23 09:00:00 No AKI
#> 8 pt1 3.9 NA 2020-10-25 21:00:00 No AKI
#> 9 pt1 3 NA 2020-10-20 09:00:00 AKI Stage 1
#> 10 pt2 3.4 NA 2020-10-18 22:00:00 No AKI
#> # … with 17 more rows
aki_pt_data %>%
mutate(aki = aki_SCr(
SCr = SCr_, dttm = dttm_, pt_id = pt_id_
)) %>%
select(pt_id_, SCr_:dttm_, aki)
#> # A tibble: 27 × 5
#> pt_id_ SCr_ bCr_ dttm_ aki
#> <chr> [mg/dl] [mg/dl] <dttm> <ord>
#> 1 <NA> 2 1.5 NA No AKI
#> 2 <NA> 2.5 1.5 NA No AKI
#> 3 <NA> 3 1.5 NA No AKI
#> 4 <NA> 3.5 1.5 NA No AKI
#> 5 <NA> 4 1.5 NA No AKI
#> 6 <NA> 4.5 1.5 NA No AKI
#> 7 pt1 3.4 NA 2020-10-23 09:00:00 No AKI
#> 8 pt1 3.9 NA 2020-10-25 21:00:00 No AKI
#> 9 pt1 3 NA 2020-10-20 09:00:00 AKI Stage 1
#> 10 pt2 3.4 NA 2020-10-18 22:00:00 No AKI
#> # … with 17 more rows
Similarly, eGFR() offers the ability to automatically select the
appropriate formula to estimate the glomerular filtration rate. If a
particular formula is required, then eGFR_adult_SCr,
eGFR_adult_SCysC, eGFR_adult_SCr_SCysC, eGFR_child_SCr,
eGFR_child_SCr_BUN, or eGFR_child_SCysC can be used.
head(eGFR_pt_data)
#> # A tibble: 6 × 10
#> SCr_ SCysC_ Age_ male_ black_ height_ BUN_ eGFR_calc_type_ eGFR_ pediatric_
#> [mg/… [mg/L] [yea… <lgl> <lgl> [m] [mg/… <chr> [mL/… <lgl>
#> 1 0.5 NA 20 FALSE FALSE NA NA eGFR_adult_SCr 139. FALSE
#> 2 NA 0.4 20 FALSE FALSE NA NA eGFR_adult_SCy… 162. FALSE
#> 3 0.5 0.4 20 FALSE FALSE NA NA eGFR_adult_SCr… 167. FALSE
#> 4 0.5 NA 30 FALSE TRUE NA NA eGFR_adult_SCr 150. FALSE
#> 5 NA 0.4 30 FALSE TRUE NA NA eGFR_adult_SCy… 155. FALSE
#> 6 0.5 0.4 30 FALSE TRUE NA NA eGFR_adult_SCr… 171. FALSE
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_
)) %>%
select(SCr_:pediatric_, eGFR)
#> # 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.
#> # … with 41 more rows, and 2 more variables: pediatric_ <lgl>,
#> # eGFR [mL/1.73m2/min]
eGFR_pt_data %>%
dplyr::mutate(eGFR = eGFR_adult_SCr(
SCr = SCr_, Age = Age_, male = male_, black = black_
)) %>%
select(SCr_:pediatric_, eGFR)
#> # 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.
#> # … with 41 more rows, and 2 more variables: pediatric_ <lgl>,
#> # eGFR [mL/1.73m2/min]
References
KDIGO Guidelines - https://kdigo.org/guidelines/
Activity
See https://alwinw.github.io/epocakir/reference/index.html for more
usage details and package reference.
alwinw/epocakir documentation built on Feb. 23, 2024, 3:30 p.m.
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epocakir
/ˈiːpɒk ə keɪ aɪ ɑː/
Clinical Coding of Patients with Kidney Disease
The epocakir package makes clinical coding of patients with kidney disease using clinical practice guidelines easy. The guidelines used are the evidence-based KDIGO guidelines. This package covers acute kidney injury (AKI), anemia, and chronic kidney disease (CKD):
-
aki_staging(): Classification of AKI staging (aki_stages) with automatic selection of:aki_bCr(): AKI based on baseline creatinineaki_SCr(): AKI based on changes in serum creatinineaki_UO(): AKI based on urine output
-
anemia(): Classification of anemia -
Classification of albuminuria (
Albuminuria_stages)Albuminuria_staging_ACR(): Albuminuria based on Albumin excretion rateAlbuminuria_staging_AER(): Albuminuria based on Albumin-to-creatinine ratio
-
eGFR(): Estimation of glomerular filtration rate with automatic selection of:eGFR_adult_SCr(): eGFR based on the 2009 CKD-EPI creatinine equationeGFR_adult_SCysC(): eGFR based on the 2012 CKD-EPI cystatin C equationeGFR_adult_SCr_SCysC(): eGFR based on the 2012 CKD-EPI creatinine-cystatin C equationeGFR_child_SCr(): eGFR based on the pediatric creatinine-based equationeGFR_child_SCr_BUN(): eGFR based on the pediatric creatinine-BUN equationeGFR_child_SCysC(): eGFR based on the pediatric cystatin C-based equation
-
GFR_staging(): Staging of GFR (GFR_stages) -
Multiple utility functions including:
conversion_factors: Conversion factors used throughout the KDIGO guidelinesas_metric(): Conversion of a measured value into metric unitsdob2age(): Calculation of age from a date of birthbinary2factor(): Conversion of binary data into factors based on a column namecombine_date_time_cols(): Combining separate date and time columns into a single date and time columncombn_changes: Generating changes between measurements
Installation
You can install the released version from CRAN with:
install.packages("epocakir")
You can install the development version from GitHub with:
# install.packages("remotes")
remotes::install_github("alwinw/epocakir")
Getting Started
library(epocakir)
library(dplyr)
library(units)
Often clinical data must be cleansed and tidied before analysis can
begin. To assist in this, several utility functions have been included.
To explore these, consider a sample clinical dataset clinical_obvs:
glimpse(clinical_obvs)
#> Rows: 3
#> Columns: 9
#> $ `Patient Number` <chr> "p10001", "p10002", "p10003"
#> $ `Admission Date` <chr> "2020-03-05", "2020-03-06", "2020-03-17"
#> $ `Admission Time` <chr> "14:01:00", "09:10:00", "12:48:00"
#> $ Discharge_date <chr> "2020-03-10", "2020-03-16", "2020-03-18"
#> $ Discharge_time <chr> "16:34:00", "18:51:00", "09:12:00"
#> $ `Date of Birth` <chr> "1956-01-09", "1997-12-04", "1973-05-28"
#> $ Male <lgl> TRUE, FALSE, TRUE
#> $ Height <dbl> 182, 161, 168
#> $ Surgery <lgl> FALSE, FALSE, TRUE
tidy_obvs <- clinical_obvs %>%
combine_date_time_cols() %>%
mutate(
Age = dob2age(`Date of Birth`),
Height = as_metric(height = set_units(as.numeric(Height), "cm"))
) %>%
binary2factor(Male, Surgery)
glimpse(tidy_obvs)
#> Rows: 3
#> Columns: 8
#> $ `Patient Number` <chr> "p10001", "p10002", "p10003"
#> $ `Admission DateTime` <dttm> 2020-03-05 14:01:00, 2020-03-06 09:10:00, 2020-03…
#> $ Discharge_DateTime <dttm> 2020-03-10 16:34:00, 2020-03-16 18:51:00, 2020-0…
#> $ `Date of Birth` <chr> "1956-01-09", "1997-12-04", "1973-05-28"
#> $ Male <ord> Male, Not_Male, Male
#> $ Height [m] 1.82 [m], 1.61 [m], 1.68 [m]
#> $ Surgery <ord> Not_Surgery, Not_Surgery, Surgery
#> $ Age <Duration> 2092780800s (~66.32 years), 770428800s (~24.41 y…
Make sure to use set_units() from the units package to convert all
measurements into unit objects for automatic unit conversion in
epocakir.
Examples
It is possible to use aki_staging() to automatically classify the
presence and staging of AKI. If a particular method is required, it is
possible to classify AKI using aki_bCr(), aki_SCr() or aki_UO().
head(aki_pt_data)
#> # A tibble: 6 × 7
#> SCr_ bCr_ pt_id_ dttm_ UO_ aki_staging_type aki_
#> [mg/dl] [mg/dl] <chr> <dttm> [ml/kg] <chr> <ord>
#> 1 2 1.5 <NA> NA NA aki_bCr No AKI
#> 2 2.5 1.5 <NA> NA NA aki_bCr AKI Stage 1
#> 3 3 1.5 <NA> NA NA aki_bCr AKI Stage 2
#> 4 3.5 1.5 <NA> NA NA aki_bCr AKI Stage 2
#> 5 4 1.5 <NA> NA NA aki_bCr AKI Stage 3
#> 6 4.5 1.5 <NA> NA NA aki_bCr AKI Stage 3
aki_staging(aki_pt_data,
SCr = "SCr_", bCr = "bCr_", UO = "UO_",
dttm = "dttm_", pt_id = "pt_id_"
)
#> [1] No AKI AKI Stage 1 AKI Stage 2 AKI Stage 2 AKI Stage 3 AKI Stage 3
#> [7] No AKI No AKI AKI Stage 1 No AKI No AKI AKI Stage 1
#> [13] No AKI No AKI No AKI AKI Stage 1 No AKI AKI Stage 2
#> [19] AKI Stage 3 AKI Stage 1 AKI Stage 3 AKI Stage 2 No AKI AKI Stage 1
#> [25] AKI Stage 3 AKI Stage 3 No AKI
#> Levels: No AKI < AKI Stage 1 < AKI Stage 2 < AKI Stage 3
aki_pt_data %>%
mutate(aki = aki_staging(
SCr = SCr_, bCr = bCr_, UO = UO_,
dttm = dttm_, pt_id = pt_id_
)) %>%
select(pt_id_, SCr_:dttm_, aki)
#> # A tibble: 27 × 5
#> pt_id_ SCr_ bCr_ dttm_ aki
#> <chr> [mg/dl] [mg/dl] <dttm> <ord>
#> 1 <NA> 2 1.5 NA No AKI
#> 2 <NA> 2.5 1.5 NA AKI Stage 1
#> 3 <NA> 3 1.5 NA AKI Stage 2
#> 4 <NA> 3.5 1.5 NA AKI Stage 2
#> 5 <NA> 4 1.5 NA AKI Stage 3
#> 6 <NA> 4.5 1.5 NA AKI Stage 3
#> 7 pt1 3.4 NA 2020-10-23 09:00:00 No AKI
#> 8 pt1 3.9 NA 2020-10-25 21:00:00 No AKI
#> 9 pt1 3 NA 2020-10-20 09:00:00 AKI Stage 1
#> 10 pt2 3.4 NA 2020-10-18 22:00:00 No AKI
#> # … with 17 more rows
aki_pt_data %>%
mutate(aki = aki_SCr(
SCr = SCr_, dttm = dttm_, pt_id = pt_id_
)) %>%
select(pt_id_, SCr_:dttm_, aki)
#> # A tibble: 27 × 5
#> pt_id_ SCr_ bCr_ dttm_ aki
#> <chr> [mg/dl] [mg/dl] <dttm> <ord>
#> 1 <NA> 2 1.5 NA No AKI
#> 2 <NA> 2.5 1.5 NA No AKI
#> 3 <NA> 3 1.5 NA No AKI
#> 4 <NA> 3.5 1.5 NA No AKI
#> 5 <NA> 4 1.5 NA No AKI
#> 6 <NA> 4.5 1.5 NA No AKI
#> 7 pt1 3.4 NA 2020-10-23 09:00:00 No AKI
#> 8 pt1 3.9 NA 2020-10-25 21:00:00 No AKI
#> 9 pt1 3 NA 2020-10-20 09:00:00 AKI Stage 1
#> 10 pt2 3.4 NA 2020-10-18 22:00:00 No AKI
#> # … with 17 more rows
Similarly, eGFR() offers the ability to automatically select the
appropriate formula to estimate the glomerular filtration rate. If a
particular formula is required, then eGFR_adult_SCr,
eGFR_adult_SCysC, eGFR_adult_SCr_SCysC, eGFR_child_SCr,
eGFR_child_SCr_BUN, or eGFR_child_SCysC can be used.
head(eGFR_pt_data)
#> # A tibble: 6 × 10
#> SCr_ SCysC_ Age_ male_ black_ height_ BUN_ eGFR_calc_type_ eGFR_ pediatric_
#> [mg/… [mg/L] [yea… <lgl> <lgl> [m] [mg/… <chr> [mL/… <lgl>
#> 1 0.5 NA 20 FALSE FALSE NA NA eGFR_adult_SCr 139. FALSE
#> 2 NA 0.4 20 FALSE FALSE NA NA eGFR_adult_SCy… 162. FALSE
#> 3 0.5 0.4 20 FALSE FALSE NA NA eGFR_adult_SCr… 167. FALSE
#> 4 0.5 NA 30 FALSE TRUE NA NA eGFR_adult_SCr 150. FALSE
#> 5 NA 0.4 30 FALSE TRUE NA NA eGFR_adult_SCy… 155. FALSE
#> 6 0.5 0.4 30 FALSE TRUE NA NA eGFR_adult_SCr… 171. FALSE
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_
)) %>%
select(SCr_:pediatric_, eGFR)
#> # 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.
#> # … with 41 more rows, and 2 more variables: pediatric_ <lgl>,
#> # eGFR [mL/1.73m2/min]
eGFR_pt_data %>%
dplyr::mutate(eGFR = eGFR_adult_SCr(
SCr = SCr_, Age = Age_, male = male_, black = black_
)) %>%
select(SCr_:pediatric_, eGFR)
#> # 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.
#> # … with 41 more rows, and 2 more variables: pediatric_ <lgl>,
#> # eGFR [mL/1.73m2/min]
References
KDIGO Guidelines - https://kdigo.org/guidelines/
Activity
See https://alwinw.github.io/epocakir/reference/index.html for more usage details and package reference.
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