Yang LB, Zhao G, Tantai XX, Xiao CL, Qin SW, Dong L, Chang DY, Jia Y, Li H. Non-invasive model for predicting esophageal varices based on liver and spleen volume. World J Clin Cases 2022; 10(32): 11743-11752 [PMID: 36405281 DOI: 10.12998/wjcc.v10.i32.11743]
Corresponding Author of This Article
Hong Li, MD, Doctor, Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an 710004, Shaanxi Province, China. hongli119@hotmail.com
Research Domain of This Article
Gastroenterology & Hepatology
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. Nov 16, 2022; 10(32): 11743-11752 Published online Nov 16, 2022. doi: 10.12998/wjcc.v10.i32.11743
Non-invasive model for predicting esophageal varices based on liver and spleen volume
Long-Bao Yang, Gang Zhao, Xin-Xing Tantai, Cai-Lan Xiao, Si-Wen Qin, Lei Dong, Dan-Yan Chang, Yuan Jia, Hong Li
Long-Bao Yang, Gang Zhao, Xin-Xing Tantai, Cai-Lan Xiao, Lei Dong, Dan-Yan Chang, Yuan Jia, Hong Li, Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710004, Shaanxi Province, China
Si-Wen Qin, Department of Medicine, Xi'an Jiaotong University, Xi'an 710004, Shaanxi Province, China
Author contributions: Yang LB, Zhao G and Dong L designed the research study; Tan-tai XX and Xiao CL performed the research; Jia Y and Chang DY contributed new reagents and analytic tools; Qing SW and Li H analyzed the data and wrote the manuscript; All authors have read and approve the final manuscript.
Supported byKey Research and Development Plan of Shaanxi Province, No. 2020SF-222.
Institutional review board statement: The study was approved by the Ethics Committee of Xi’an Jiaotong University, Shaanxi, China (NO. 2017-445).
Informed consent statement: Informed consent was waived by the Ethics Committee of Xi’an Jiaotong University as patients were identified retrospectively, according to institutional review board exempt protocols.
Conflict-of-interest statement: There are no conflicts of interest to declare.
Data sharing statement: Technical appendix, statistical code, and dataset available from the corresponding author at hongli119@hotmail.com.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Hong Li, MD, Doctor, Department of Gastroenterology, The Second Affiliated Hospital of Xi'an Jiaotong University, No. 157 Xiwu Road, Xi'an 710004, Shaanxi Province, China. hongli119@hotmail.com
Received: August 28, 2022 Peer-review started: August 28, 2022 First decision: October 4, 2022 Revised: October 7, 2022 Accepted: October 18, 2022 Article in press: October 18, 2022 Published online: November 16, 2022
ARTICLE HIGHLIGHTS
Research background
Although there are reports of models predicting esophageal varices; however, there were no models based on the standard liver and spleen volume calculation formula.
Research motivation
It is highly important to identify virus patients with esophageal varices (EVs) and guide them for gastroscopy, and a non-invasive predictive model can be used to identify EVs.
Research objectives
A non-invasive predictive model for EVs based on liver and spleen volume in viral cirrhosis patients.
Research methods
A cross-sectional study based on viral cirrhosis crowd were conducted in the Second Affiliated Hospital of Xi'an Jiaotong University. By collecting the participants’ basic information and clinical data of the, we derived the independent risk factors and established the prediction model of EVs. We compared the established model with others. Area under the receiver operating characteristic curve, calibration plot and decision curve analysis were used to test the discriminating ability, calibration ability and clinical practicability in both internal and external validation group.
Research results
The portal vein diameter, the liver and spleen volume, and volume change rate were successfully used to establish the predictive model, which showed better predictive value than other models. The model indicating good discriminating ability, calibration ability and clinical practicability in both modelling and external validation group.
Research conclusions
The developed model is a credible predictor of EVs with high specificity, calibrability and clinical efficacy.
Research perspectives
Further studies to confirm this model’s potential using larger sample sizes are recommended. Besides, there is need to develop predictive models with high diagnostic accuracy, while considering the limitations herein.
