Retrospective Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
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
Abstract
BACKGROUND

Acute kidney injury (AKI) after surgery appears to increase the risk of death in patients with liver cancer. In recent years, machine learning algorithms have been shown to offer higher discriminative efficiency than classical statistical analysis.

AIM

To develop prediction models for AKI after liver cancer resection using machine learning techniques.

METHODS

We screened 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. The AKI definition used was consistent with the Kidney Disease: Improving Global Outcomes. We included in our analysis preoperative data such as demographic characteristics, laboratory findings, comorbidities, and medication, as well as perioperative data such as duration of surgery. Computerized algorithms used for model development included logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGboost), and decision tree (DT). Feature importance was also ranked according to its contribution to model development.

RESULTS

AKI events occurred in 296 patients (12.1%) within 7 d after surgery. Among the original models based on machine learning techniques, the RF algorithm had optimal discrimination with an area under the curve value of 0.92, compared to 0.87 for XGBoost, 0.90 for DT, 0.90 for SVM, and 0.85 for LR. The RF algorithm also had the highest concordance-index (0.86) and the lowest Brier score (0.076). The variable that contributed the most in the RF algorithm was age, followed by cholesterol, and surgery time.

CONCLUSION

Machine learning algorithms are highly effective in discriminating patients at high risk of developing AKI. The successful application of machine learning models may help guide clinical decisions and help improve the long-term prognosis of patients.

Keywords: Machine learning; Liver cancer; Surgery; Acute kidney injury; Prediction

Core Tip: Acute kidney injury (AKI) is a relatively common complication after liver surgery and has a negative impact on long-term patient prognosis. Early detection and timely intervention are key in order to minimize the negative impact of AKI. Machine learning has become increasingly better integrated with clinical medicine. In our retrospective study, we established a real-time prediction model based on machine learning algorithms. The final models showed high power to discriminate AKI events.