Bredt LC, Peres LAB, Risso M, Barros LCAL. Risk factors and prediction of acute kidney injury after liver transplantation: Logistic regression and artificial neural network approaches . World J Hepatol 2022; 14(3): 570-582 [PMID: 35582300 DOI: 10.4254/wjh.v14.i3.570]
Corresponding Author of This Article
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
Research Domain of This Article
Transplantation
Article-Type of This Article
Retrospective Cohort Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Hepatol. Mar 27, 2022; 14(3): 570-582 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, Luis Alberto Batista Peres, Michel Risso, Leandro Cavalcanti de Albuquerque Leite Barros
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
ARTICLE HIGHLIGHTS
Research background
Acute kidney injury (AKI) post-liver transplantation (LT) is a serious complication, and its prediction with validated tools is crucial.
Research motivation
To improve the perioperative management of patient candidates for LT.
Research objectives
To identify the risk factors for AKI after deceased-donor liver transplantation (DDLT) and validate a prediction tool for this complication.
Research methods
Logistic regression (LR) analysis for predictor identification, and comparative analysis of artificial neural network (ANN) and LR prediction performance were performed.
Research results
The severity of liver disease, preexisting kidney dysfunction, marginal grafts, hemodynamic instability, massive blood transfusion, and SL were predictors of postoperative AKI, and ANN had better prediction performance than LR.
Research conclusions
ANN has better performance than the classical LR for AKI prediction after DDLT.
Research perspectives
A risk score of AKI after DDLT can be developed according to these identified predictors.
