Retrospective Study
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 7, 2020; 26(21): 2839-2851
Published online Jun 7, 2020. doi: 10.3748/wjg.v26.i21.2839
Non-invasive prediction model for high-risk esophageal varices in the Chinese population
Long-Bao Yang, Jing-Yuan Xu, Xin-Xing Tantai, Hong Li, Cai-Lan Xiao, Cai-Feng Yang, Huan Zhang, Lei Dong, Gang Zhao
Long-Bao Yang, Jing-Yuan Xu, Xin-Xing Tantai, Hong Li, Cai-Lan Xiao, Cai-Feng Yang, Huan Zhang, Lei Dong, Gang Zhao, Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, Shaanxi Province, China
Author contributions: All authors helped to perform the research; Dong L and Zhao G completed the project; Xiao CL and Zhang H completed the data collection; Tantai XX, Xu JY, and Yang CF completed the data analysis; Yang LB and Li H completed writing of the article.
Institutional review board statement: This study has been approved by the Ethics Committee of The Second Affiliated Hospital of Xi'an Jiaotong University.
Informed consent statement: This study is a retrospective study; thus, the ethics committee has exempted the informed consent of the patients.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Gang Zhao, MD, PhD, Doctor, Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157, Xiwu Road, Xi'an 710004, Shaanxi Province, China. zhaogang799@126.com
Received: December 28, 2019
Peer-review started: December 28, 2019
First decision: January 13, 2020
Revised: March 26, 2020
Accepted: April 21, 2020
Article in press: April 21, 2020
Published online: June 7, 2020
Processing time: 160 Days and 22.2 Hours
ARTICLE HIGHLIGHTS
Research background

Several models for predicting high-risk esophageal varices (HEVs) have been reported; however, models that are based on liver and spleen volume calculation formula in HEVs have not been reported.

Research motivation

HEVs are EVs that have a high risk of bleeding, and the establishment of a non-invasive predictive model will be useful for the early identification of HEVs. These patients will benefit if necessary measures are taken in a timely manner.

Research objectives

This present study established a non-invasive prediction model based on the liver and spleen volume calculation formula for predicting HEVs in patients with viral cirrhosis.

Research methods

Eighty-six EVs patients with viral cirrhosis, from October 2017 to December 2018, were included at the Second Affiliated Hospital of Xi’an Jiaotong University. By reviewing the medical records, required data were collected for. The impact of each parameter on HEVs was analyzed by univariate and multivariate analyses, the data from which were employed to establish a non-invasive prediction model. Then the established prediction model was compared with LSPS, VRI, APRI, and AAR. The discriminating ability, calibration ability, and clinical efficacy of the established model were verified in both the modeling group and the external validation group.

Research results

After univariate and multivariate analysis, liver-spleen volume ratio, spleen volume change rate, and aspartate aminotransferase were successfully used to establish the non-invasive prediction model for HEVs. The new model could better predict HEVs compared with LSPS, VRI, APRI, and AAR. The discriminating ability, calibration ability, and clinical efficacy of the new model were verified.

Research conclusions

The non-invasive prediction model for predicting HEVs is a reliable model for predicting HEVs and has clinical applicability.

Research perspectives

The predictive value of the new model needs to be confirmed in a large number of virus cirrhosis patients with EVs. Predictive models with high accuracy need to be established taking into account the limitations of the new model.