Healthc Inform Res.  2017 Oct;23(4):277-284. 10.4258/hir.2017.23.4.277.

Prediction of Kidney Graft Rejection Using Artificial Neural Network

• Tapak L
• Hamidi O
• Amini P
• Poorolajal J

• KMID: 2396419
• DOI: http://doi.org/10.4258/hir.2017.23.4.277

Abstract

OBJECTIVES
Kidney transplantation is the best renal replacement therapy for patients with end-stage renal disease. Several studies have attempted to identify predisposing factors of graft rejection; however, the results have been inconsistent. We aimed to identify prognostic factors associated with kidney transplant rejection using the artificial neural network (ANN) approach and to compare the results with those obtained by logistic regression (LR).
METHODS
The study used information regarding 378 patients who had undergone kidney transplantation from a retrospective study conducted in Hamadan, Western Iran, from 1994 to 2011. ANN was used to identify potential important risk factors for chronic nonreversible graft rejection.
RESULTS
Recipients' age, creatinine level, cold ischemic time, and hemoglobin level at discharge were identified as the most important prognostic factors by ANN. The ANN model showed higher total accuracy (0.75 vs. 0.55 for LR), and the area under the ROC curve (0.88 vs. 0.75 for LR) was better than that obtained with LR.
CONCLUSIONS
The results of this study indicate that the ANN model outperformed LR in the prediction of kidney transplantation failure. Therefore, this approach is a promising classifier for predicting graft failure to improve patients' survival and quality of life, and it should be further investigated for the prediction of other clinical outcomes.

Keyword

Kidney Transplantation; Graft Rejection; Logistic Models; Neural Networks; Data Mining

MeSH Terms

Causality
Cold Ischemia
Creatinine
Data Mining
Graft Rejection*
Humans
Iran
Kidney Failure, Chronic
Kidney Transplantation
Kidney*
Logistic Models
Quality of Life
Renal Replacement Therapy
Retrospective Studies
Risk Factors
ROC Curve
Transplants*
Creatinine

Figure

• 

  Figure 1 Normalized importance of the variables in artificial neural network (ANN).

• 

  Figure 2 Area under ROC curve (AUC) for the performed methods.

Reference

1. Levey AS, Inker LA, Coresh J. Chronic kidney disease in older people. JAMA. 2015; 314(6):557–558.

2. Mills KT, Xu Y, Zhang W, Bundy JD, Chen CS, Kelly TN, et al. Abstract MP40: Global burden of chronic kidney disease in 2010: a pooling analysis of population-based data worldwide. Circulation. 2015; 131:Suppl 1. AMP40.

3. Saatchi M, Poorolajal J, Amirzargar MA, Mahjub H, Esmailnasab N. Long-term survival rate of kidney graft and associated prognostic factors: a retrospective cohort study, 1994-2011. Ann Transplant. 2013; 18:153–160.

4. White SL, Chadban SJ, Jan S, Chapman JR, Cass A. How can we achieve global equity in provision of renal replacement therapy? Bull World Health Organ. 2008; 86(3):229–237.

5. Hamidi O, Poorolajal J, Farhadian M, Tapak L. Identifying important risk factors for survival in kidney graft failure patients using random survival forests. Iran J Public Health. 2016; 45(1):27–33.

6. Hesketh CC, Knoll GA, Molnar AO, Tsampalieros A, Zimmerman DL. Vitamin D and kidney transplant outcomes: a protocol for a systematic review and meta-analysis. Syst Rev. 2014; 3:64.

7. Han J, Kamber M. Data mining: concepts and techniques. Amsterdam: Elsevier/Morgan Kaufmann;2000.

8. Tu JV. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. J Clin Epidemiol. 1996; 49(11):1225–1231.

9. Delen D, Oztekin A, Tomak L. An analytic approach to better understanding and management of coronary surgeries. Decis Support Syst. 2012; 52(3):698–705.

10. Agresti A, Kateri M. Categorical data analysis. In : Lovric M, editor. International encyclopedia of statistical science. Heidelberg: Springer;2011. p. 206–208.

11. Linder R, Konig IR, Weimar C, Diener HC, Poppl SJ, Ziegler A. Two models for outcome prediction. Methods Inf Med. 2006; 45(5):536–540.

12. Caocci G, Baccoli R, Littera R, Orru S, Carcassi C, La Nasa G. Comparison between an artificial neural network and logistic regression in predicting long term kidney transplantation outcome. In : Suzuki K, editor. Artificial neural networks: architectures and applications. Rijeka, Croatia: InTech;2013.

13. Luck M, Sylvain T, Cardinal H, Lodi A, Bengio Y. Deep learning for patient-specific kidney graft survival analysis [Internet]. Ithaca (NY): arXiv.org;2017. cited at 2017 Oct 1. Available from: https://arxiv.org/abs/1705.10245.

14. Ayat S, Farahani HA, Aghamohamadi M, Alian M, Aghamohamadi S, Kazemi Z. A comparison of artificial neural networks learning algorithms in predicting tendency for suicide. Neural Comput Appl. 2013; 23(5):1381–1386.

15. Lofaro D, Greco R, Papalia T, Bonofiglio R. Increasing levels of hemoglobin improve renal transplantation outcomes. Transplant Proc. 2011; 43(4):1036–1038.

16. Prasad GVR, Ananth S, Palepu S, Huang M, Nash MM, Zaltzman JS. Commercial kidney transplantation is an important risk factor in long-term kidney allograft survival. Kidney Int. 2016; 89(5):1119–1124.

17. Sert I, Colak H, Tugmen C, Dogan SM, Karaca C. The effect of cold ischemia time on delayed graft function and acute rejection in kidney transplantation. Saudi J Kidney Dis Transpl. 2014; 25(5):960–966.

18. Gonzalez-Molina M, Burgos D, Cabello M, Ruiz-Esteban P, Rodriguez MA, Gutierrez C, et al. Impact of immunosuppression treatment on the improvement in graft survival after deceased donor renal transplantation: a long-term cohort study. Nefrologia. 2014; 34(5):570–578.

19. Rostami Z, Shafighi N, Baghersad MM, Einollahi B. Risk factors for immediate anemia in renal transplant recipients: a single-center experience. Transplant Proc. 2011; 43(2):581–583.

20. Oetting WS, Guan W, Schladt DP, Wildebush WA, Becker J, Thyagarajan B, et al. Telomere length of recipients and living kidney donors and chronic graft dysfunction in kidney transplants. Transplantation. 2014; 97(3):325–329.

21. Gondos A, Dohler B, Brenner H, Opelz G. Kidney graft survival in Europe and the United States: strikingly different long-term outcomes. Transplantation. 2013; 95(2):267–274.

22. Ghadiani MH, Peyrovi S, Mousavinasab SN, Jalalzadeh M. Delayed graft function, allograft and patient srvival in kidney transplantation. Arab J Nephrol Transplant. 2012; 5(1):19–24.

23. Guliyev O, Sayin B, Uyar ME, Genctoy G, Sezer S, Bal Z, et al. High-grade proteinuria as a cardiovascular risk factor in renal transplant recipients. Transplant Proc. 2015; 47(4):1170–1173.

24. Ounissi M, Cherif M, Abdallah TB, Bacha M, Hedri H, Abderrahim E, et al. Risk factors and consequences of delayed graft function. Saudi J Kidney Dis Transpl. 2013; 24(2):243–246.

25. Lin CC, Chan HH, Huang CY, Yang NS. Predictive models for pre-operative diagnosis of rotator cuff tear: a comparison study of two methods between logistic regression and artificial neural network. Appl Mech Mater. 2014; (595):263–268.

26. Shi HY, Lee KT, Lee HH, Ho WH, Sun DP, Wang JJ, et al. Comparison of artificial neural network and logistic regression models for predicting in-hospital mortality after primary liver cancer surgery. PLoS One. 2012; 7(4):e35781.

27. Chen H, Zhang J, Xu Y, Chen B, Zhang K. Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans. Expert Syst Appl. 2012; 39(13):11503–11509.

28. Tapak L, Mahjub H, Hamidi O, Poorolajal J. Real-data comparison of data mining methods in prediction of diabetes in Iran. Healthc Inform Res. 2013; 19(3):177–185.

29. Lau L, Kankanige Y, Rubinstein B, Jones R, Christophi C, Muralidharan V, Bailey J. Machine-learning algorithms predict graft failure after liver transplantation. Transplantation. 2017; 101(4):e125–e132.

30. Shaikhina T, Lowe D, Daga S, Briggs D, Higgins R, Khovanova N. Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed Signal Process Control. 2017; 02. 09. [Epub]. http://www.sciencedirect.com/science/article/pii/S1746809417300204.