medigraphic.com
SPANISH

Revista de Nefrología, Diálisis y Trasplante

ISSN 0326-3428 (Print)
Órgano de difusión científica de la Asociación Nefrológica de Buenos Aires
  • Contents
  • View Archive
  • Information
    • General Information        
    • Directory
  • Publish
    • Instructions for authors        
  • medigraphic.com
    • Home
    • Journals index            
    • Register / Login
  • Mi perfil

2022, Number 3

<< Back Next >>

Rev Nefrol Dial Traspl 2022; 42 (3)

Method for predicting the probability of kidney transplantation for patients on the waiting list in Colombia

Zhang GC, Lamprea BN, López-Kleine L
Full text How to cite this article

Language: Spanish
References: 14
Page: 225-239
PDF size: 972.08 Kb.


Key words:

organ transplant, organ distribution criteria, synthetic data, organ donation system.

ABSTRACT

Introduction: The criteria for organ distribution in Colombia establish an initial local distribution, then regional and finally national. In December 2019, 2,822 people were on the kidney transplant waiting list in Colombia, assigned mostly to the Bogotá and IPS regions with the largest waiting lists. This high concentration of patients could be generating unwanted effects on the opportunity that patients have to receive a kidney transplant. Objectives: In this paper we seek to study, based on synthetic data generated with the information available from the INS, the probability of organ allocation, identifying the most informative variables and proposing a method to calculate the probability of allocation for a given patient on the waiting list of organs. Material and methods: The adjustment of a model based on decision trees is presented, which showed a high precision and allows the prediction of the probability of obtaining an organ. Results: Time, transplant IPS and blood group were identified as the most informative variables. Likewise, there are differences in the time it takes to obtain kidney transplants between regions and between transplanting IPS due to the effect of the size of their waiting list. Conclusions: The proposed method allows us to identify the importance of the variables that define obtaining an organ. Finally, for a given patient, it is possible to estimate the probability of being classified in one of the outcome categories.


REFERENCES

  1. Tonelli M, Wiebe N, Knoll G, et al. Systematic review:kidney transplantation compared with dialysis inclinically relevant outcomes. Am J Transplant. 2011;11(10):2093-2109.

  2. Instituto Nacional de Salud. 2018. Criterios deAsignación para Trasplante Renal en Colombia.Fecha de consulta: Marzo 2021. Disponible: http://www.ins.gov.co/Direcciones/RedesSaludPublica/DonacionOrganosYTejidos/Paginas/default.aspx

  3. Informe Nacional de Salud. Red Nacional de Donacióny Trasplantes 2019. Informe Ejecutivo. Disponible:https://www.ins.gov.co/BibliotecaDigital/informeejecutivo-red-donacion-y-trasplantes-2019.pdf

  4. Instituto Nacional de Salud. Informe anual red dedonación y trasplantes.Colombia, 2018. Disponible:https://www.ins.gov.co/BibliotecaDigital/informeanual-red-de-donacion-trasplantes-2018.pdf

  5. Departamento Nacional de Estadística. Población deColombia es de 48,2 millones de habitantes, según elDANE. Disponible en: https://id.presidencia.gov.co/Paginas/prensa/2019/190704-Poblacion-de-Colombiaes-de-48-2-millones-habitantes-segun-DANE.Aspx

  6. R Core Team (2020). R: A language and environmentfor statistical computing. R Foundation for StatisticalComputing, Vienna, Austria. Disponible: https://www.R-project.org/.

  7. Beltrán M, Ayala M, Jara J. Frecuencia de grupossanguíneos y factor Rh en donantes de sangre, Colombia,1996. Biomédica. 1999; 19(1): 39-44.

  8. Allele Frecuency Net Database . Disponible: http://www.allelefrequencies.net/

  9. Tangirala S. Evaluating the Impact of GINI Index andInformation Gain on Classification using DecisionTree Classifier Algorithm*. Int J Adv Comput Sci Appl.2020;11(2): 612-19.

  10. Elkan Ch. Evaluating Classifiers. University ofCalifornia, San Diego, 18 de enero de 2011. Disponibleen: https://web.archive.org/web/20111218192652/http:/cseweb.ucsd.edu/~elkan/250B/classifiereval.pdf

  11. Efron B. Bootstrap Methods: Another Look at theJackknife. The Annals of Statistics. 1979; 7(1):1–26.

  12. Lima B. A call for open data of renal transplantationin Portugal. Port J Nephrol Hypert. 2017; 31(3): 155-7.

  13. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel,B. Thirion, O. Grisel, J. Vanderplas. Scikit-learn:machine learning in Python J. Mach. Learn. Res., 12(2011), pp. 2825-2830.

  14. Canty A, Ripley BD (2021). boot: Bootstrap R (S-Plus)Functions. R package version 1.3-28.




2020     |     www.medigraphic.com

Mi perfil

C?MO CITAR (Vancouver)

Rev Nefrol Dial Traspl. 2022;42