Lai RM, Wang MM, Lin XY, Zheng Q, Chen J. Clinical value of predictive models based on liver stiffness measurement in predicting liver reserve function of compensated chronic liver disease. World J Gastroenterol 2022; 28(42): 6045-6055 [PMID: 36405384 DOI: 10.3748/wjg.v28.i42.6045]
Corresponding Author of This Article
Jing Chen, MD, Chief Physician, Professor, Department of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, No. 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China. mykelchen@sina.com
Research Domain of This Article
Infectious Diseases
Article-Type of This Article
Retrospective Cohort 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/
Rui-Min Lai, Xiao-Yu Lin, Qi Zheng, Jing Chen, Department of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, Fujian Province, China
Miao-Miao Wang, Department of Endocrinology, The 910th Hospital of The Joint Service Support Force, Quanzhou 362000, Fujian Province, China
Author contributions: Lai RM and Zheng Q conceived and designed the study; Wang MM and Lin XY collected clinical data of the patients and contributed to the data analysis; Lai RM and Chen J wrote the manuscript; and all authors approved the final version of the manuscript.
Supported byStartup Fund for Scientific Research of Fujian Medical University, No. 2018QH1052; and Fujian Health Research Talents Training Program, No. 2019-1-42.
Institutional review board statement: This retrospective study was approved by the ethics committee at the First Affiliated Hospital of Fujian Medical University, China.
Informed consent statement: Patients were not required to give informed consent to the study as the analysis used anonymous data that were obtained after each patient agreed to treatment by written consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The original anonymous dataset is available on request from the corresponding author at mykelchen@sina.com.
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 items.
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: Jing Chen, MD, Chief Physician, Professor, Department of Hepatology, Hepatology Research Institute, The First Affiliated Hospital, Fujian Medical University, No. 20 Chazhong Road, Taijiang District, Fuzhou 350005, Fujian Province, China. mykelchen@sina.com
Received: July 1, 2022 Peer-review started: July 1, 2022 First decision: August 1, 2022 Revised: August 13, 2022 Accepted: October 14, 2022 Article in press: October 14, 2022 Published online: November 14, 2022 Processing time: 132 Days and 5.2 Hours
Core Tip
Core Tip: This study aimed to establish predictive models of liver stiffness measurement (LSM) in patients with compensated chronic liver disease based on LSM and evaluate their clinical value. The results showed that the new models had a good predictive performance for liver reserve function (LRF). The area under the curve of the models was higher than that of the model for end-stage liver disease, albumin-bilirubin grade and prothrombin time international normalized ratio to albumin ratio. Moreover, the predictive performance of the new models was validated in a prospective cohort. We believe that these models could replace the indocyanine green (ICG) clearance test to assess LRF, especially in patients who are unable to undergo ICG testing.
