Observational Study
Copyright ©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
Prediction of hepatic inflammation in chronic hepatitis B patients with a random forest-backward feature elimination algorithm
Ji-Yuan Zhou, Liu-Wei Song, Rong Yuan, Xiao-Ping Lu, Gui-Qiang Wang
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
Processing time: 102 Days and 18.8 Hours
Abstract
BACKGROUND

Persistent liver inflammatory damage is the main risk factor for developing liver fibrosis, cirrhosis, and even hepatocellular carcinoma in chronic hepatitis B (CHB) patients. Thus, accurate prediction of the degree of liver inflammation is a high priority and a growing medical need.

AIM

To build an effective and robust non-invasive model for predicting hepatitis B-related hepatic inflammation.

METHODS

A total of 650 treatment-naïve CHB (402 HBeAg-positive and 248 HBeAg-negative) patients who underwent liver biopsy were enrolled in this study. Histological inflammation grading was assessed by the Ishak scoring system. Serum quantitative hepatitis B core antibody (qAnti-HBc) levels and 21 immune-related inflammatory factors were measured quantitatively using a chemiluminescent microparticle immunoassay. A backward feature elimination (BFE) algorithm utilizing random forest (RF) was used to select optional features and construct a combined model. The diagnostic abilities of the model or variables were evaluated based on the estimated area under the receiver operating characteristics curve (AUROC) and compared using the DeLong test.

RESULTS

Four features were selected to predict moderate-to-severe inflammation in CHB patients using the RF-BFE method. These predictive features included qAnti-HBc, ALT, AST, and CXCL11. Spearman’s correlation analysis indicated that serum qAnti-HBc, ALT, AST, and CXCL11 levels were positively correlated with the histology activity index (HAI) score. These selected features were incorporated into the model to establish a novel model named I-3A index. The AUROC [0.822; 95% confidence interval (CI): 0.790-0.851] of the I-3A index was significantly increased compared with qAnti-HBc alone (0.760, 95%CI: 0.724-0.792, P < 0.0001) in all CHB patients. The use of an I-3A index cutoff value of 0.41 produced a sensitivity of 69.17%, specificity of 81.44%, and accuracy of 73.8%. Additionally, the I-3A index showed significantly improved diagnostic performance for predicting moderate-to-severe inflammation in HBeAg-positive and HBeAg-negative CHB patients (0.829, 95%CI: 0.789-0.865 and 0.810, 95%CI: 0.755-0.857, respectively).

CONCLUSION

The selected features of the I-3A index constructed using the RF-BFE algorithm can effectively predict moderate-to-severe liver inflammation in CHB patients.

Keywords: Hepatic inflammation; Machine learning; Quantitative hepatitis B core antibody; CXCL11; Diagnostic efficiency

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.