From 602d74f18f191dd5c61cee91f70157fab4dccd3b Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Wed, 4 May 2022 11:41:27 +0000 Subject: [PATCH] Re-build README.Rmd --- README.md | 195 +++++++++++++++++++++++++++--------------------------- 1 file changed, 97 insertions(+), 98 deletions(-) diff --git a/README.md b/README.md index 09e8dbd..dcad594 100644 --- a/README.md +++ b/README.md @@ -30,51 +30,51 @@ the evidence-based [KDIGO guidelines](https://kdigo.org/guidelines/). This package covers acute kidney injury (AKI), anemia, and chronic kidney disease (CKD): -- `aki_staging()`: Classification of AKI staging (`aki_stages`) with + - `aki_staging()`: Classification of AKI staging (`aki_stages`) with automatic selection of: + + - `aki_bCr()`: AKI based on baseline creatinine + - `aki_SCr()`: AKI based on changes in serum creatinine + - `aki_UO()`: AKI based on urine output - - `aki_bCr()`: AKI based on baseline creatinine - - `aki_SCr()`: AKI based on changes in serum creatinine - - `aki_UO()`: AKI based on urine output + - `anemia()`: Classification of anemia -- `anemia()`: Classification of anemia - -- Classification of albuminuria (`Albuminuria_stages`) - - - `Albuminuria_staging_ACR()`: Albuminuria based on Albumin + - Classification of albuminuria (`Albuminuria_stages`) + + - `Albuminuria_staging_ACR()`: Albuminuria based on Albumin excretion rate - - `Albuminuria_staging_AER()`: Albuminuria based on + - `Albuminuria_staging_AER()`: Albuminuria based on Albumin-to-creatinine ratio -- `eGFR()`: Estimation of glomerular filtration rate with automatic + - `eGFR()`: Estimation of glomerular filtration rate with automatic selection of: - - - `eGFR_adult_SCr()`: eGFR based on the 2009 CKD-EPI creatinine + + - `eGFR_adult_SCr()`: eGFR based on the 2009 CKD-EPI creatinine equation - - `eGFR_adult_SCysC()`: eGFR based on the 2012 CKD-EPI cystatin C + - `eGFR_adult_SCysC()`: eGFR based on the 2012 CKD-EPI cystatin C equation - - `eGFR_adult_SCr_SCysC()`: eGFR based on the 2012 CKD-EPI + - `eGFR_adult_SCr_SCysC()`: eGFR based on the 2012 CKD-EPI creatinine-cystatin C equation - - `eGFR_child_SCr()`: eGFR based on the pediatric creatinine-based + - `eGFR_child_SCr()`: eGFR based on the pediatric creatinine-based equation - - `eGFR_child_SCr_BUN()`: eGFR based on the pediatric + - `eGFR_child_SCr_BUN()`: eGFR based on the pediatric creatinine-BUN equation - - `eGFR_child_SCysC()`: eGFR based on the pediatric cystatin + - `eGFR_child_SCysC()`: eGFR based on the pediatric cystatin C-based equation -- `GFR_staging()`: Staging of GFR (`GFR_stages`) - -- Multiple utility functions including: + - `GFR_staging()`: Staging of GFR (`GFR_stages`) - - `conversion_factors`: Conversion factors used throughout the + - Multiple utility functions including: + + - `conversion_factors`: Conversion factors used throughout the KDIGO guidelines - - `as_metric()`: Conversion of a measured value into metric units - - `dob2age()`: Calculation of age from a date of birth - - `binary2factor()`: Conversion of binary data into factors based + - `as_metric()`: Conversion of a measured value into metric units + - `dob2age()`: Calculation of age from a date of birth + - `binary2factor()`: Conversion of binary data into factors based on a column name - - `combine_date_time_cols()`: Combining separate date and time + - `combine_date_time_cols()`: Combining separate date and time columns into a single date and time column - - `combn_changes`: Generating changes between measurements + - `combn_changes`: Generating changes between measurements ## Installation @@ -131,13 +131,13 @@ glimpse(tidy_obvs) #> Rows: 3 #> Columns: 8 #> $ `Patient Number` "p10001", "p10002", "p10003" -#> $ `Admission DateTime` 2020-03-05 14:01:00, 2020-03-06 09:10:00, 202... -#> $ Discharge_DateTime 2020-03-10 16:34:00, 2020-03-16 18:51:00, 202... +#> $ `Admission DateTime` 2020-03-05 14:01:00, 2020-03-06 09:10:00, 2020-03… +#> $ Discharge_DateTime 2020-03-10 16:34:00, 2020-03-16 18:51:00, 2020-0… #> $ `Date of Birth` "1956-01-09", "1997-12-04", "1973-05-28" #> $ Male Male, Not_Male, Male #> $ Height [m] 1.82 [m], 1.61 [m], 1.68 [m] #> $ Surgery Not_Surgery, Not_Surgery, Surgery -#> $ Age 2092780800s (~66.32 years), 770428800s (~... +#> $ Age 2092780800s (~66.32 years), 770428800s (~24.41 y… ``` Make sure to use `set_units()` from the `units` package to convert all @@ -152,15 +152,15 @@ possible to classify AKI using `aki_bCr()`, `aki_SCr()` or `aki_UO().` ``` r head(aki_pt_data) -#> # A tibble: 6 x 7 -#> SCr_ bCr_ pt_id_ dttm_ UO_ aki_staging_type aki_ -#> [mg/dl] [mg/dl] [ml/kg] -#> 1 2.0 1.5 NA NA aki_bCr No AKI -#> 2 2.5 1.5 NA NA aki_bCr AKI Stag~ -#> 3 3.0 1.5 NA NA aki_bCr AKI Stag~ -#> 4 3.5 1.5 NA NA aki_bCr AKI Stag~ -#> 5 4.0 1.5 NA NA aki_bCr AKI Stag~ -#> 6 4.5 1.5 NA NA aki_bCr AKI Stag~ +#> # A tibble: 6 × 7 +#> SCr_ bCr_ pt_id_ dttm_ UO_ aki_staging_type aki_ +#> [mg/dl] [mg/dl] [ml/kg] +#> 1 2 1.5 NA NA aki_bCr No AKI +#> 2 2.5 1.5 NA NA aki_bCr AKI Stage 1 +#> 3 3 1.5 NA NA aki_bCr AKI Stage 2 +#> 4 3.5 1.5 NA NA aki_bCr AKI Stage 2 +#> 5 4 1.5 NA NA aki_bCr AKI Stage 3 +#> 6 4.5 1.5 NA NA aki_bCr AKI Stage 3 aki_staging(aki_pt_data, SCr = "SCr_", bCr = "bCr_", UO = "UO_", @@ -179,40 +179,40 @@ aki_pt_data %>% dttm = dttm_, pt_id = pt_id_ )) %>% select(pt_id_, SCr_:dttm_, aki) -#> # A tibble: 27 x 5 +#> # A tibble: 27 × 5 #> pt_id_ SCr_ bCr_ dttm_ aki #> [mg/dl] [mg/dl] -#> 1 2.0 1.5 NA No AKI +#> 1 2 1.5 NA No AKI #> 2 2.5 1.5 NA AKI Stage 1 -#> 3 3.0 1.5 NA AKI Stage 2 +#> 3 3 1.5 NA AKI Stage 2 #> 4 3.5 1.5 NA AKI Stage 2 -#> 5 4.0 1.5 NA AKI Stage 3 +#> 5 4 1.5 NA AKI Stage 3 #> 6 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 3.0 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 +#> 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 x 5 +#> # A tibble: 27 × 5 #> pt_id_ SCr_ bCr_ dttm_ aki #> [mg/dl] [mg/dl] -#> 1 2.0 1.5 NA No AKI +#> 1 2 1.5 NA No AKI #> 2 2.5 1.5 NA No AKI -#> 3 3.0 1.5 NA No AKI +#> 3 3 1.5 NA No AKI #> 4 3.5 1.5 NA No AKI -#> 5 4.0 1.5 NA No AKI +#> 5 4 1.5 NA No AKI #> 6 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 3.0 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 +#> 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 @@ -223,16 +223,15 @@ particular formula is required, then `eGFR_adult_SCr`, ``` r head(eGFR_pt_data) -#> # A tibble: 6 x 10 -#> SCr_ SCysC_ Age_ male_ black_ height_ BUN_ eGFR_calc_type_ -#> [mg/dl] [mg/L] [years] [m] [mg/dl] -#> 1 0.5 NA 20 FALSE FALSE NA NA eGFR_adult_SCr -#> 2 NA 0.4 20 FALSE FALSE NA NA eGFR_adult_SCy~ -#> 3 0.5 0.4 20 FALSE FALSE NA NA eGFR_adult_SCr~ -#> 4 0.5 NA 30 FALSE TRUE NA NA eGFR_adult_SCr -#> 5 NA 0.4 30 FALSE TRUE NA NA eGFR_adult_SCy~ -#> 6 0.5 0.4 30 FALSE TRUE NA NA eGFR_adult_SCr~ -#> # ... with 2 more variables: eGFR_ [mL/1.73m2/min], pediatric_ +#> # A tibble: 6 × 10 +#> SCr_ SCysC_ Age_ male_ black_ height_ BUN_ eGFR_calc_type_ eGFR_ pediatric_ +#> [mg/… [mg/L] [yea… [m] [mg/… [mL/… +#> 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_", @@ -256,42 +255,42 @@ eGFR_pt_data %>% male = male_, black = black_, pediatric = pediatric_ )) %>% select(SCr_:pediatric_, eGFR) -#> # A tibble: 51 x 11 -#> SCr_ SCysC_ Age_ male_ black_ height_ BUN_ eGFR_calc_type_ -#> [mg/dl] [mg/L] [years] [m] [mg/dl] -#> 1 0.5 NA 20 FALSE FALSE NA NA eGFR_adult_SCr -#> 2 NA 0.4 20 FALSE FALSE NA NA eGFR_adult_SCy~ -#> 3 0.5 0.4 20 FALSE FALSE NA NA eGFR_adult_SCr~ -#> 4 0.5 NA 30 FALSE TRUE NA NA eGFR_adult_SCr -#> 5 NA 0.4 30 FALSE TRUE NA NA eGFR_adult_SCy~ -#> 6 0.5 0.4 30 FALSE TRUE NA NA eGFR_adult_SCr~ -#> 7 0.5 NA 20 FALSE FALSE NA NA eGFR_adult_SCr -#> 8 NA 1.2 20 FALSE FALSE NA NA eGFR_adult_SCy~ -#> 9 0.5 1.2 20 FALSE FALSE NA NA eGFR_adult_SCr~ -#> 10 0.5 NA 30 FALSE TRUE NA NA eGFR_adult_SCr -#> # ... with 41 more rows, and 3 more variables: eGFR_ [mL/1.73m2/min], -#> # pediatric_ , eGFR [mL/1.73m2/min] +#> # A tibble: 51 × 11 +#> SCr_ SCysC_ Age_ male_ black_ height_ BUN_ eGFR_calc_type_ eGFR_ +#> [mg/dl] [mg/L] [years] [m] [mg/dl] [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_ , +#> # 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 x 11 -#> SCr_ SCysC_ Age_ male_ black_ height_ BUN_ eGFR_calc_type_ -#> [mg/dl] [mg/L] [years] [m] [mg/dl] -#> 1 0.5 NA 20 FALSE FALSE NA NA eGFR_adult_SCr -#> 2 NA 0.4 20 FALSE FALSE NA NA eGFR_adult_SCy~ -#> 3 0.5 0.4 20 FALSE FALSE NA NA eGFR_adult_SCr~ -#> 4 0.5 NA 30 FALSE TRUE NA NA eGFR_adult_SCr -#> 5 NA 0.4 30 FALSE TRUE NA NA eGFR_adult_SCy~ -#> 6 0.5 0.4 30 FALSE TRUE NA NA eGFR_adult_SCr~ -#> 7 0.5 NA 20 FALSE FALSE NA NA eGFR_adult_SCr -#> 8 NA 1.2 20 FALSE FALSE NA NA eGFR_adult_SCy~ -#> 9 0.5 1.2 20 FALSE FALSE NA NA eGFR_adult_SCr~ -#> 10 0.5 NA 30 FALSE TRUE NA NA eGFR_adult_SCr -#> # ... with 41 more rows, and 3 more variables: eGFR_ [mL/1.73m2/min], -#> # pediatric_ , eGFR [mL/1.73m2/min] +#> # A tibble: 51 × 11 +#> SCr_ SCysC_ Age_ male_ black_ height_ BUN_ eGFR_calc_type_ eGFR_ +#> [mg/dl] [mg/L] [years] [m] [mg/dl] [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_ , +#> # eGFR [mL/1.73m2/min] ``` ## References @@ -309,7 +308,7 @@ commit](https://img.shields.io/github/last-commit/alwinw/epocakir) bytes](https://img.shields.io/github/repo-size/alwinw/epocakir) ![Total Lines](https://img.shields.io/tokei/lines/github/alwinw/epocakir) ------------------------------------------------------------------------- +----- See for more usage details and package reference.