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©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 7, 2021; 27(21): 2910-2920
Published online Jun 7, 2021. doi: 10.3748/wjg.v27.i21.2910
Published online Jun 7, 2021. doi: 10.3748/wjg.v27.i21.2910
Prediction of hepatic inflammation in chronic hepatitis B patients with a random forest-backward feature elimination algorithm
Ji-Yuan Zhou, Department of Gastroenterology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou 510260, Guangdong Province, China
Liu-Wei Song, School of Public Health, Xiamen University, Xiamen 361005, Fujian Province, China
Rong Yuan, Xiao-Ping Lu, Intervention and Cell Therapy Center, Peking University Shenzhen Hospital, Shenzhen 518036, Guangdong Province, China
Gui-Qiang Wang, Department of Infectious Disease, Peking University First Hospital, Beijing 100034, China
Author contributions: Zhou JY designed the study, analyzed the data, and contributed to writing the manuscript; Song LW and Lu XP performed the ELISA experiments and contributed to discussions; Yuan R performed the RF-BFE analysis; Wang GQ provided patient data and overall direction; all authors gave final approval of the version to be published and agree to be accountable for all aspects of the work.
Supported by the China Mega-Project for Infectious Diseases , No. 2017ZX10203202 ; and the Guangdong Basic and Applied Basic Research Foundation , No. 2019A1515110060 .
Institutional review board statement: The study was approved by the Human Research Committee of Peking University First Hospital.
Informed consent statement: All patients gave informed consent.
Conflict-of-interest statement: No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of item.
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: Gui-Qiang Wang, MD, Chairman, Director, Professor, Department of Infectious Disease, Peking University First Hospital, No. 8 Xishiku Street, Xicheng District, Beijing 100034, China. john131212@126.com
Received: February 13, 2021
Peer-review started: February 13, 2021
First decision: March 14, 2021
Revised: April 1, 2021
Accepted: April 20, 2021
Article in press: April 20, 2021
Published online: June 7, 2021
Peer-review started: February 13, 2021
First decision: March 14, 2021
Revised: April 1, 2021
Accepted: April 20, 2021
Article in press: April 20, 2021
Published online: June 7, 2021
Core Tip
Core Tip: We aimed to propose an effective backward feature elimination algorithm utilizing random forest to select optimal features and construct a novel non-invasive model for predicting hepatitis B-related hepatic inflammation based on a large, multicenter cohort. The results indicated that the I-3A index constructed based on the selected features significantly improved the diagnostic efficiency of quantitative hepatitis B core antibody alone for predicting moderate-to-severe inflammation. Additionally, the I-3A index showed high diagnostic accuracy for moderate-to-severe inflammation in both HBeAg-positive and HBeAg-negative chronic hepatitis B patients.