2022, Number 3
How can we predict the kidney graft failure of Portuguese patients?
Cerqueira S, Campelos MR, Leite A, Pires S, Torres PL, Diniz H, Sampaio S, Figueiredo A
Full text
Language: English
References: 6
Page: 189-198
PDF size: 518.37 Kb.
ABSTRACT
Background: The gap between offer and need for a kidney transplant (KT) has been increasing. The Kidney Donor Profile Index (KDPI) is a measure of “organ quality” and allows estimation of graft survival, but could not apply to all populations. Knowledge of our kidney donor and recipient population is vital to adjust transplant strategies. Methods: We performed a retrospective evaluation of donors and recipients of KT regarding two kidney transplant units: Centro Hospitalar Universitário de Coimbra, CHUC (Coimbra, Portugal) and Centro Hospitalar Universitário de São João, CHUSJ (Porto, Portugal), between 2013 and 2018. We then did statistical analysis and modeling, correlating these KT outcomes with donor and recipient characteristics, including KDPI. Artificial intelligence methods were performed to determine the best predictors of graft survival. Results: We analyzed a total of 808 kidney donors and 829 recipients of KT. The association between KDPI and graft dysfunction was only moderate. The decision tree machine learning algorithm proved to be better at predicting graft failure than artificial neural networks. Multinomial logistic regression revealed recipient age as an important prognostic factor for graft loss. Conclusions: In this Portuguese cohort, KDPI was not a good measure of KT survival, although it correlated with GFR 1 year post-transplant. The decision tree proved to be the best algorithm to predict graft failure. Age of the recipient was the most important predictor of graft dysfunction.REFERENCES