LivTransplantEvolution

Description

End-Stage Liver Disease (ESLD) is a terminal diagnosis that encompasses advanced liver disease, liver failure, and decompensated cirrhosis. Liver transplantation, either from living or deceased donors, remains the only life saving therapeutic intervention available. Transplanted organs can be Living Donor Liver transplantation (LDLT) or Deceased Donor Liver transplantation (DDLT). DDLT is the major form of transplantation in Canada accounting for 78% of Liver Transplants (LT) in 2021. Various allocaiton systems exist but the main one is the Model for End-stage Liver Disease (MELD) score, a linear model that uses the log of the Serum Creatinine, International Normalized Ratio (INR) and Bilirubin. It is the the adopted method of severity calculation in the united states and Canada.

The MELD score informs the 90 day mortality of that patient following this scheme: MELD is ≤9 = 1.9% Mortality, MELD is 10–19 = 6.0% Mortality, MELD is 20–29 = 19.6% Mortality MELD is 30–39 = 52.6% Mortality, MELD is ≥40 = 71.3% Mortality. MELD-na is an augmentation to the MELD score calculation that takes into account Serum Sodium as it was shown that taking sodium into account for the MELD score calculation would improve it somewhat. MELD 3.0 is an improvement on the MELD-na score that tries to compensate for equity issues identified with the MELD and MELD-na risk scores such as sex-based disparity against women. MELD 3.0 takes into account the the albumin biomarker and the gender of the patient.

The Scientific Registry of Transplant Recipients (SRTR) database is the database containing information largely collected by the Organ Procurement and Transplantation Network (OPTN) in the United States. This very Large database contains the information for all patients who have been added to a transplant wait list in the United States. Moreover the patient data within the SRTR database is the base for a great deal of the transplantation research going on in North America.

The goal of LivTransplantEvolution is to be an analysis tool for clinicians using the SRTR database and conducting research on Liver transplantation who might not have the greatest familiarity with technical analysis and computation. What it will do, is take the Raw Liver transplantation patient data from the stathiist_liin data file from the SRTR database. It will clean this data, to only keep the relevant biomarkers to calculate the various MELD scores. It will analyze the Liver transplantation relevant biomarkers, specifically those used in the MELD, MELD-na and MELD-3.0 risk score calculations (Creatinine, Bilirubin INR, Albumin & Sodium). It will visualize the trajectories of those risk scores, compare them and extract the biomarkers most impactful of each longitudinal change. These plotted trajectories and calculated MELD scores will help inform clinicans about the potential future patient state and the trajectory of the patient health journey.

• R Development Version: 4.1.2
• Platform: x86_64-apple-darwin20 (64-bit)
• Running under: macOS Monterey 12.0.1

Installation

You can install the development version of LivTransplantEvolution from GitHub with:

install.packages("devtools")
library("devtools")
devtools::install_github("Mouaid-Alim/LivTransplantEvolution", build_vignettes = TRUE)
library("LivTransplantEvolution")

To run the ShinyApp:

LivTransplantEvolution::runLivTransplantEvolution()

Overview

ls("package:LivTransplantEvolution")
data(package = "LivTransplantEvolution") # optional
browseVignettes("LivTransplantEvolution")

LivTransplantEvolution contains 6 functions.

prepareData: Takes in a csv file for the STATHIIST_LIIN data file from the SRTR database, prepares and cleans up the raw patient data from the data file to extract only the coloumns containing Liver transplantation relevant clinical serum biomarker data, specifically those used in the MELD, MELD-na and MELD 3.0 risk score calculations. The columns returned are Patient_ID, Date, ALbumin, Bilirubin, INR, Serum_Creatinine, Serum_Sodium, Gender and Dialysis_In_Past_Week

calcMELD: A function that calculates the MELD risk score given the input values for Creatinine, Bilirubin, Inr and TRUE or FALSE for whether the patient has had dialysis in the past week

calcMELDna: A function that calculates the MELD-na risk score given the input values for Creatinine, Bilirubin, Inr, Sodium and TRUE or FALSE for whether the patient has had dialysis in the past week

calcMELDthree: A function that calculates the MELD-3.0 risk score given the input values for Creatinine, Bilirubin, Inr, Sodium and ‘F’ or ‘M’ for the gender of the patient

createMELDdf: A function that that will take a dataframe containing the cleaned up patient data generated by the prepareData function. It Will create a dataframe from the cleaned up data that contains longitudinal MELD risk score values for MELD, MELD-na and MELD-3.0 for every entry adn every patient

visualizeMELD: A function that will take in the dataframe created by the createMELDdf function and visualize the longitudinal MELD score trajectories for the MELD, MELD-na and MELD-3.0 risk scores together for every patient

Package Workflow:

Contributions

This Package is Created by Mouaid Alim.

The prepareData function uses the reader package to read the csv file and uses the dplyr package to create the final dataframe with correct column names. The createMELDdf function uses the dplyr packaage to create the MELD dataframe, uses the purr package to efficciently calculate the MELD scores for the dataframe, uses the tidyr package to clean up the input dataframe and uses the devtools package to import the LivTransplantEvolution package to be able to call the buil-in functions for calculating the various MELD scores. The visualizeMELD function uses the tidyr to reshape the data for plotting and uses the ggplot2 package for plotting the MELD visuals. Formula used to calculate MELD score in calcMELD function adapted from paper by Kamath et al. Formula used to calculate MELD-na risk score in calcMELDna function adapted from paper by Biggins, S. W. et al. Formula used to calculate MELD-3.0 risk score in calcMELDna function adapted from paper by Kim, W. R. et al. The TestingPackage package was used as guide for overall package structure. The shiny package was used to create the shiny app for this package. The shinyalert package was used in the creation of some components of the shiny app as well.

References

1. Potosek, J., Curry, M., Buss, M. & Chittenden, E. Integration of palliative care in end-stage liver disease and liver transplantation. J. Palliat. Med. 17, 1271–1277 (2014).
2. Summary statistics on organ transplants, wait-lists and donors. Cihi.ca https://www.cihi.ca/en/summary-statistics-on-organ-transplants-wait-lists-and-donors.
3. Kwong, A. J. et al. OPTN/SRTR 2019 annual data report: Liver. Am. J. Transplant 21 Suppl 2, 208–315 (2021).
4. Liver transplant risk stratification. R-project.org https://cran.r-project.org/web/packages/transplantr/vignettes/liver_recipient_scoring.html.
5. The SRTR database. Srtr.org https://www.srtr.org/about-the-data/the-srtr-database/.
6. Kamath, P. et al. A model to predict survival in patients with end-stage liver disease. Gastroenterology 120, A76–A77 (2001).
7. Kim, W. R. et al. MELD 3.0: The model for end-Stage Liver Disease updated for the modern era. Gastroenterology 161, 1887-1895.e4 (2021).
8. Slack, A., Yeoman, A. & Wendon, J. Renal dysfunction in chronic liver disease. Crit. Care 14, 214 (2010).
9. Biggins, S. W. et al. Serum sodium predicts mortality in patients listed for liver transplantation. Hepatology 41, 32–39 (2005).
10. Guerra Ruiz, A. R. et al. Measurement and clinical usefulness of bilirubin in liver disease. Adv. Lab. Med. 2, 352–361 (2021).
11. Kalas, M. A., Chavez, L., Leon, M., Taweesedt, P. T. & Surani, S. Abnormal liver enzymes: A review for clinicians. World J. Hepatol. 13, 1688–1698 (2021).
12. Tufoni, M., Zaccherini, G., Caraceni, P. & Bernardi, M. Albumin: Indications in chronic liver disease. United European Gastroenterol. J. 8, 528–535 (2020).
13. Wickham H, François R, Henry L, Müller K, Vaughan D (2023). dplyr: A Grammar of Data Manipulation. R package version 1.1.4, https://github.com/tidyverse/dplyr, https://dplyr.tidyverse.org.
14. Wickham H, Henry L (2023). purrr: Functional Programming Tools. R package version 1.0.2, https://github.com/tidyverse/purrr, https://purrr.tidyverse.org/.
15. Wickham H, Hester J, Chang W, Bryan J (2022). devtools: Tools to Make Developing R Packages Easier. https://devtools.r-lib.org/, https://github.com/r-lib/devtools.
16. Wickham H, Vaughan D, Girlich M (2023). tidyr: Tidy Messy Data. R package version 1.3.0, https://github.com/tidyverse/tidyr, https://tidyr.tidyverse.org.
17. Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
18. Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2023). shiny: Web Application Framework for R. R package version 1.8.0.9000, https://github.com/rstudio/shiny, https://shiny.posit.co/.
19. Attali D, Edwards T (2021). *_shinyalert: Easily Create Pretty Popup Messages* *(Modals) in ‘Shiny’_.* R package version 3.0.0, https://CRAN.R-project.org/package=shinyalert.
20. Silva, A. (2022) TestingPackage: An Example R Package For BCB410H. Unpublished. URL https://github.com/anjalisilva/TestingPackage.”

Acknowledgements

This package was developed as part of an assessment for 2023 BCB410H: Applied Bioinformatics course at the University of Toronto, Toronto, CANADA. LivTransplantEvolution welcomes issues, enhancement requests, and other contributions. To submit an issue, use the GitHub issues.

Package Structure

The tree structure for the package can be seen below:

- LivTransplantEvolution
  |- LivTransplantEvolution.Rproj
  |- DESCRIPTION
  |- NAMESPACE
  |- LICENSE
  |- README
  |- data
    |- cleanedUpSamplePatientData.rda
    |- sampleMELDdf.rda
  |- inst
    CITATION
    |- extdata
      |- Package_Workflow.png
      |- MELD_scores.png
      |- updated_meld_patient_data.csv
      |- cleaned_up_raw_data.png
      |- raw_patient_dataset.png
    |- shiny-scripts 
        |- app.R
  |- man
    |- runLivTransplantEvolution.Rd
    |- calcMELDna.Rd
    |- calcMELD.Rd
    |- cleanedUpSamplePatientData.Rd
    |- prepareData.Rd
    |- sampleMELDdf.Rd
    |- visualizeMELD.Rd
    |- calcMELDThree.Rd
    |- createMELDdf.Rd
  |- R
    |- runLivTransplantEvolution.R
    |- calcMELDna.R
    |- calcMELD.R
    |- prepareData.R
    |- visualizeMELD.R
    |- calcMELDThree.R
    |- createMELDdf.R
    |- data.R
  |- vignettes
    |- LivTransplantEvolutionVignette.Rmd
  |- tests
    |- testthat.R
    |- testthat
      |- test-calcMELDna.R
      |- test-calcMELD.R
      |- test-prepareData.R
      |- test-visualizeMELD.R
      |- test-calcMELDThree.R
      |- test-createMELDdf.R