Dong JF, Xue Q, Chen T, Zhao YY, Fu H, Guo WY, Ji JS. Machine learning approach to predict acute kidney injury after liver surgery. World J Clin Cases 2021; 9(36): 11255-11264 [PMID: 35071556 DOI: 10.12998/wjcc.v9.i36.11255]
Corresponding Author of This Article
Jun-Song Ji, MM, PhD, Associate Professor, Department of Organ Transplantation, Changzheng Hospital, Navy Medical University, No. 415 Fengyang Road, Huangpu District, Shanghai 200003, China. 974938677@qq.com
Research Domain of This Article
Urology & Nephrology
Article-Type of This Article
Retrospective 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 Clin Cases. Dec 26, 2021; 9(36): 11255-11264 Published online Dec 26, 2021. doi: 10.12998/wjcc.v9.i36.11255
Machine learning approach to predict acute kidney injury after liver surgery
Jun-Feng Dong, Qiang Xue, Ting Chen, Yuan-Yu Zhao, Hong Fu, Wen-Yuan Guo, Jun-Song Ji
Jun-Feng Dong, Yuan-Yu Zhao, Hong Fu, Wen-Yuan Guo, Jun-Song Ji, Department of Organ Transplantation, Changzheng Hospital, Navy Medical University, Shanghai 200003, China
Qiang Xue, Department of Neurosurgery, Eastern Hepatobiliary Surgery Hospital, Navy Medical University, Shanghai 200082, China
Ting Chen, Department of Intensive Rehabilitation, Zhabei Central Hospital, Shanghai 200070, China
Author contributions: Dong JF, Xue Q, and Chen T contributed equally to this work; Guo WY and Ji JS should be considered co-corresponding authors; Dong JF, Xue Q, and Chen T were responsible for conceptualization, data curation, methodology, and wrote the original draft; Zhao YY and Fu H were responsible for visualization and software; Guo WY and Ji JS were responsible for validation, supervision, reviewing and editing the manuscript; all authors approved the final submission.
Institutional review board statement: This study was approved by the Ethics Committee of Navy Medical University.
Informed consent statement: The data were not involved in the patients’ privacy information, so the informed consent was waived by the Ethics Committee of Navy Medical University.
Conflict-of-interest statement: The authors have no related conflicts of interest to disclose.
Data sharing statement: No additional data are available.
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: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Jun-Song Ji, MM, PhD, Associate Professor, Department of Organ Transplantation, Changzheng Hospital, Navy Medical University, No. 415 Fengyang Road, Huangpu District, Shanghai 200003, China. 974938677@qq.com
Received: August 10, 2021 Peer-review started: August 10, 2021 First decision: September 2, 2021 Revised: September 15, 2021 Accepted: November 3, 2021 Article in press: November 3, 2021 Published online: December 26, 2021 Processing time: 135 Days and 4.3 Hours
ARTICLE HIGHLIGHTS
Research background
Recently, machine learning has proven helpful in the interpretation of medical results and has potential for helping guide diagnosis and treatment, ultimately improving patient outcomes.
We aimed to develop prediction models for AKI after liver cancer resection based on machine learning techniques.
Research methods
A total of 2450 patients who had undergone primary hepatocellular carcinoma resection at Changzheng Hospital, Shanghai City, China, from January 1, 2015 to August 31, 2020 were screened. Patients were randomly assigned to the training and the test sets at a ratio of 7:3. The training set was used for model development and optimization, while the test set was used for model validation and evaluation.
Research results
AKI events occurred in 296 patients (12.1%) after surgery. Among the original models based on machine learning techniques, the random forest (RF) algorithm had optimal discrimination with an area under the curve value of 0.92, compared to 0.87 for extreme gradient boosting, 0.90 for decision tree, 0.90 for support vector machine, and 0.85 for logistic regression. The RF algorithm also had the highest concordance-index (0.86) and the lowest Brier score (0.076). The variables that contributed the most in the RF algorithm were age, cholesterol, and surgery time.
Research conclusions
Machine learning technology can accurately predict AKI after hepatectomy.
Research perspectives
In the era of personalized medicine, our model based on machine learning can discriminate patients at high risk for AKI, thus helping guide clinical decisions and facilitating prospective interventions for high-risk individuals.
