Retrospective Cohort Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 14, 2022; 28(42): 6045-6055
Published online Nov 14, 2022. doi: 10.3748/wjg.v28.i42.6045
Clinical value of predictive models based on liver stiffness measurement in predicting liver reserve function of compensated chronic liver disease
Rui-Min Lai, Miao-Miao Wang, Xiao-Yu Lin, Qi Zheng, Jing Chen
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 by Startup 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
ARTICLE HIGHLIGHTS
Research background

There are no obvious clinical symptoms in chronic liver disease (CLD) patients at the early stage, but their liver reserve function (LRF) may be impaired. Early evaluation of LRF is of great help in identifying disease progression. Assessment of LRF is essential for predicting the prognosis of patients with CLD and determines the extent of liver resection in patients with hepatocellular carcinoma (HCC).

Research motivation

Liver function impairment is the primary determinant in the development of post-hepatectomy liver failure. There are no obvious clinical symptoms in CLD patients at Child-Turcotte-Pugh A stage, but their LRF may be impaired. Due to impossible implementation of the indocyanine green (ICG) clearance test in some CLD patients, a new method to accurately assess LRF is needed.

Research objectives

This study aimed to establish noninvasive models of LRF assessment based on LSM. The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort. The new models had a good predictive performance on LRF and could replace the ICG clearance test, especially in the patients who are unable to undergo ICG testing.

Research methods

Clinical data from 360 patients with compensated CLD were retrospectively collected and analyzed in the training cohort. The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort (132 patients). The areas under the ROC curve (AUCs) were measured and compared to evaluate the discrimination ability of different models.

Research results

Our study defined the indocyanine green retention rate at 15 min (ICGR15) ≥ 10% as mildly impaired LRF and ICGR15 ≥ 20% as severely impaired LRF. We constructed predictive models of LRF, named the mLPaM and sLPaM, which involved only LSM, prothrombin time international normalized ratio to albumin ratio, age and model for end-stage liver disease. The AUC of the mLPaM model (0.855, 0.872, respectively) and sLPaM model (0.869, 0.876, respectively) were higher than that of other methods in the two cohorts. In addition, the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort.

Research conclusions

Our study found that the new models had a good predictive performance for LRF and could replace the ICG clearance test, especially in patients who are unable to undergo ICG testing.

Research perspectives

This was not a multicenter study and most of the CLD patients in this study were Asians. Therefore, a multi-center prospective cohort study could further evaluate the performance of the predictive models, and the models in patients of other ethnicities need further investigation. The predictive value of the models in patients with a decompensated stage need further evaluation.