Copyright
©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Mar 27, 2022; 14(3): 570-582
Published online Mar 27, 2022. doi: 10.4254/wjh.v14.i3.570
Published online Mar 27, 2022. 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
Luis Cesar Bredt, Department of Surgical Oncology and Hepatobilary Surgery, Unioeste, Cascavel 85819-110, Paraná, Brazil
Luis Alberto Batista Peres, Department of Nephrology, Unioeste, Cascavel 85819-110, Paraná, Brazil
Michel Risso, Department of Internal Medicine, Assis Gurgacz University, Cascavel 85000, Paraná, Brazil
Leandro Cavalcanti de Albuquerque Leite Barros, Department of Hepatobiliary Surgery, Unioeste, Cascavel 85819-110, Paraná, Brazil
Author contributions: Bredt LC, Peres LAB, Risso M, and Barros LCAL contributed equally to this study with regard to conception and design, literature review and analysis, manuscript drafting, critical revision, and editing, and approval of the final version.
Institutional review board statement: The study was approved by the Research Ethics Board at Assis Gurgacz University (No. 4.190.165). The study was performed according to the ethical guidelines of the 1975 Declaration of Helsinki.
Conflict-of-interest statement: All authors that contributed equally to this manuscript declare no potential conflicts of interest and no financial support.
Data sharing statement: All authors declare that the original anonymous dataset is available on request from the corresponding author (lcbredt@gmail.com).
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Noncommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Luis Cesar Bredt, FRCS (Gen Surg), MD, PhD, Full Professor, Surgeon, Department of Surgical Oncology and Hepatobilary Surgery, Unioeste, Tancredo Neves Avenue, Cascavel 85819-110, Paraná, Brazil. lcbredt@gmail.com
Received: September 27, 2021
Peer-review started: September 27, 2021
First decision: December 2, 2021
Revised: December 10, 2021
Accepted: February 16, 2022
Article in press: February 16, 2022
Published online: March 27, 2022
Processing time: 178 Days and 12.6 Hours
Peer-review started: September 27, 2021
First decision: December 2, 2021
Revised: December 10, 2021
Accepted: February 16, 2022
Article in press: February 16, 2022
Published online: March 27, 2022
Processing time: 178 Days and 12.6 Hours
Core Tip
Core Tip: This study aimed to identify the risk factors of acute kidney injury (AKI) after deceased-donor liver transplantation and compare the performance of artificial neural network (ANN) with that of logistic regression (LR) analysis to predict this complication. LR analysis revealed the following predictors of AKI: Previous kidney dysfunction, marginal grafts, intra-operative arterial hypotension, massive blood transfusion, and serum lactate. ANN prediction had better performance than LR in this scenario.