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Kidney transplantation

Boris Bikbov, Scientific-Tools.Org

2025-03-31

ktx.Rmd

Kidney transplantation

kidney.epi R package includes functions which useful for kidney transplantation, and a sample data frame.

See also: [Scientific-Tools.Org], [kidneyepidemiology.org]

Dataframes

  • ktx.data - contains data for 10 kidney transplant patients (see description in documentation).
library(kidney.epi)
#> The kidney.epi package is made with care by the research consultancy Scientific-Tools.Org.
#> Contact us at https://Scientific-Tools.Org or via 'maintainer("kidney.epi")' for data analysis or software development.
head(ktx.data)
#>   ptid rec.age don.age don.height don.weight don.ethnicity don.hypertension
#> 1  149    62.5      68        155       60.0         White              Yes
#> 2  385    53.5      43        180       90.0         White              N/A
#> 3  299    69.6      78        168       70.0         White              Yes
#> 4  224    74.3      68        180      113.0         White              Yes
#> 5  330    71.5      73        171       68.0         White              Yes
#> 6  428    67.1      72        163       68.6         White               No
#>   don.diabetes don.causeofdeath don.creatinine don.hcv don.dcdstatus don.sex
#> 1           No  cerebrovascular           0.68       N            No    Male
#> 2          N/A           trauma           0.76       N           Yes  Female
#> 3           No  cerebrovascular           0.82       N            No    Male
#> 4           No  cerebrovascular           1.97       N            No  Female
#> 5           No           trauma           0.63       N            No    Male
#> 6           No  cerebrovascular           1.13       N           Yes  Female

Functions

The years from 2014 to 2024 are supported. More is underway. Scientific-Tools.Org provides research and consulting services for policy makers, industry, scientists, patient organizations, and citizens. This package is developed in our free time or with your support.

Calculate KDPI and KDRI with ktx.kdpi.optn

Function ktx.kdpi.optn calculates Kidney Donor Risk Index (KDRI) and Kidney Donor Profile Index (KDPI) based on the algorithm of US Organ Procurement and Transplantation Network.

In case you use this function and kidney.epi package for preparation of a manuscript, please use the following citation: “Bikbov B. R open source programming code for calculation of the Kidney Donor Profile Index and Kidney Donor Risk Index. Kidney Disease. 2018; 4:269–272 doi:10.1159/000492427”.

You can use ktx.kdpi.optn function either for calculation of KDPI and KDRI for a single patient, or for a dataset.

To calculate for a single patient, use the following syntax:

# call ktx.kdpi.optn function, and directly set parameters values
ktx.kdpi.optn(age = 60, 
  height_cm = 168, 
  weight_kg = 93, 
  ethnicity = "White", 
  hypertension = "yes", 
  diabetes = "no", 
  causeofdeath = "roadinjury", 
  creatinine = 1.4, 
  hcv = "negative", 
  dcdstatus = "no", 
  creatinine_units = "mg/dl", 
  return_output_type = "KDRI_Rao")
#> For the calculations of KDRI and KDPI the  2024  year is used for mapping values, KDRI scaling factor, as well as chances of hypertension and diabetes in case if they were unknown for donor.
#> [1] 1.8

To calculate for a multiple patients in a dataset, use the following syntax:

# copy internal dataframe ktx.data from the kidney.epiR package to your data frame
mydata <- ktx.data

# calculate Kidney Donor Profile Index (KDPI) using the latest available OPTN mapping values
mydata$kdpi <- ktx.kdpi.optn ( age = mydata$don.age,
  height_cm = mydata$don.height, weight_kg = mydata$don.weight,
  ethnicity = mydata$don.ethnicity, hypertension = mydata$don.hypertension,
  diabetes = mydata$don.diabetes, causeofdeath = mydata$don.causeofdeath,
  creatinine = mydata$don.creatinine, hcv = mydata$don.hcv,
  dcdstatus = mydata$don.dcd, creatinine_units = "mg/dl",
  # which param to return
  return_output_type = "KDPI",
  # customize all labels used in the dataframe
  # label for Afroamerican ethnicity 
  label_afroamerican = c ("Afroamerican"),
  # label for a positive history of hypertension
  label_hypertension_positive = c ("Yes", "YES"),
  # label for an unknown history of hypertension
  label_hypertension_unknown = "N/A", # if missing values defined unknown history then use "NA" (with quotes!)
  # label for a positive history of diabetes
  label_diabetes_positive = c ("Yes", "YES"),
  # label for an unknown history of diabetes
  label_diabetes_unknown = "N/A", # if missing values defined unknown history then use "NA" (with quotes!)
  # label for a cause of death due to cerebrovascular/stroke
  label_causeofdeath = c ("cerebrovascular"),
  # label for a positive hcv status
  label_hcv_positive = c ("positive"),
  # label for an unknown, not done, indeterminate, or pending hcv status
  label_hcv_unknown = "NA", # if missing values defined unknown history then use "NA" (with quotes!)
  # label for a donation after circulatory death status
  label_dcdstatus = c ("Yes")
) 
#> For the calculations of KDRI and KDPI the  2024  year is used for mapping values, KDRI scaling factor, as well as chances of hypertension and diabetes in case if they were unknown for donor.

# show descriptive stat for the calculated values
summary(mydata$kdpi)
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   37.00   93.25   95.50   89.10   97.00  100.00

Take in account that the labels used in parameters of the function have to correspond to the labels used in your data frame. In the example above the labels for positive history of hypertension are defined as label_hypertension_positive = c (“Yes”, “YES”) that means in the data frame it could be either “Yes” or “YES”. In case you use different labeling in your data frame, define this appropriately. For example, if you define a positive history of hypertension as 1 and negative history as 0, you have to change the labeling in parameters of the function to label_hypertension_positive = c (1).

Important notice on NA (not available values)
Be careful with labeling of missing/unknown data for a history of hypertension and diabetes.
If donors with an unknown history of hypertension in your data file has a standard R missing value NA, set the parameter label_hypertension_unknown = “NA” (with quotes!).
If donors with an unknown history of hypertension in your data file defined as “no data”, set the parameter label_hypertension_unknown = “no data”.
If donors with an unknown history of hypertension in your data file defined as 999, set the parameter label_hypertension_unknown = 999.

Note that by default KDPI and KDRI are calculated by the latest available OPTN revision (by default the parameter is mapping_values_year = “latest”). If you wish to change the reference year, set the mapping_values_year equal to the year. To see for which years the OPTN revision is available in the R package, use function ktx.kdpi.optn.show.years().

Show years available in the OPTN mapping table with ktx.kdpi.optn.show.years

Shows which years are available in the R package for the OPTN mapping table, KDRI scaling factor, etc. These years could be defined in the mapping_values_year parameter of the ktx.kdpi.optn function.

ktx.kdpi.optn.show.years()
#> The following years are presented in tables with OPTN mapping values, KDRI scaling factor, etc. 
#>  [1] 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
#> You can use any of this years in the mapping_values_year parameter of the ktx.kdpi.optn function for mapping calculations of KDPI and KDRI.

References