Risk factors and prediction of acute kidney injury after liver transplantation: Logistic regression and artificial neural network approaches

Luis Cesar Bredt; Luis Alberto Batista Peres; Michel Risso; Leandro Cavalcanti de Albuquerque Leite Barros

doi:10.4254/wjh.v14.i3.570

Risk factors and prediction of acute kidney injury after liver transplantation: Logistic regression and artificial neural network approaches

World J Hepatol. 2022 Mar 27;14(3):570-582. doi: 10.4254/wjh.v14.i3.570.

Authors

Luis Cesar Bredt¹, Luis Alberto Batista Peres², Michel Risso³, Leandro Cavalcanti de Albuquerque Leite Barros⁴

Affiliations

¹ Department of Surgical Oncology and Hepatobilary Surgery, Unioeste, Cascavel 85819-110, Paraná, Brazil. lcbredt@gmail.com.
² Department of Nephrology, Unioeste, Cascavel 85819-110, Paraná, Brazil.
³ Department of Internal Medicine, Assis Gurgacz University, Cascavel 85000, Paraná, Brazil.
⁴ Department of Hepatobiliary Surgery, Unioeste, Cascavel 85819-110, Paraná, Brazil.

Abstract

Background: Acute kidney injury (AKI) has serious consequences on the prognosis of patients undergoing liver transplantation. Recently, artificial neural network (ANN) was reported to have better predictive ability than the classical logistic regression (LR) for this postoperative outcome.

Aim: To identify the risk factors of AKI after deceased-donor liver transplantation (DDLT) and compare the prediction performance of ANN with that of LR for this complication.

Methods: Adult patients with no evidence of end-stage kidney dysfunction (KD) who underwent the first DDLT according to model for end-stage liver disease (MELD) score allocation system was evaluated. AKI was defined according to the International Club of Ascites criteria, and potential predictors of postoperative AKI were identified by LR. The prediction performance of both ANN and LR was tested.

Results: The incidence of AKI was 60.6% (n = 88/145) and the following predictors were identified by LR: MELD score > 25 (odds ratio [OR] = 1.999), preoperative kidney dysfunction (OR = 1.279), extended criteria donors (OR = 1.191), intraoperative arterial hypotension (OR = 1.935), intraoperative massive blood transfusion (MBT) (OR = 1.830), and postoperative serum lactate (SL) (OR = 2.001). The area under the receiver-operating characteristic curve was best for ANN (0.81, 95% confidence interval [CI]: 0.75-0.83) than for LR (0.71, 95%CI: 0.67-0.76). The root-mean-square error and mean absolute error in the ANN model were 0.47 and 0.38, respectively.

Conclusion: The severity of liver disease, pre-existing kidney dysfunction, marginal grafts, hemodynamic instability, MBT, and SL are predictors of postoperative AKI, and ANN has better prediction performance than LR in this scenario.

Keywords: Acute kidney injury; Artificial neural network; Liver transplantation; Logistic regression; Machine learning.