4.6 Article

Inflammation response and liver stiffness: predictive model of regression of hepatic stiffness after sustained virological response in cirrhotics patients with chronic hepatitis C

期刊

CLINICAL AND EXPERIMENTAL MEDICINE
卷 21, 期 4, 页码 587-597

出版社

SPRINGER-VERLAG ITALIA SRL
DOI: 10.1007/s10238-021-00708-w

关键词

Liver cirrhosis; Elastography; Hepatitis C; Stiffness regression

资金

  1. FAPESP [2016/25416-3]
  2. Coordination for the Improvement of Higher Education Personnel - Brazil (CAPES) [001]

向作者/读者索取更多资源

In cirrhotic patients with chronic hepatitis C, the most important biomarkers for predicting regression are TCD4+ lymphocytes. Non-regressors (NR) have lower levels of liver stiffness since baseline compared to regressors (R), while NK cells are increased in NR. The differences in the profile of circulating immune cells between R and NR allow for the development of a predictive model for regression of liver stiffness after SVR, although further validation in larger patient cohorts is needed.
Cirrhotic patients with chronic hepatitis C should be monitored for the evaluation of liver function and screening of hepatocellular carcinoma even after sustained virological response (SVR). The stage of inflammatory resolution and regression of fibrosis is likely to happen, once treatment and viral clearance are achieved. However, liver examinations by elastography show that 30-40% of patients do not exhibit a reduction of liver stiffness. This work was a cohort study in cirrhotic patients whose purpose was to identify immunological factors involved in the regression of liver stiffness in chronic hepatitis C and characterize possible serum biomarkers with prognostic value. The sample universe consisted of 31 cirrhotic patients who underwent leukocyte immunophenotyping, quantification of cytokines/chemokines and metalloproteinase inhibitors in the pretreatment (M1) and in the evaluation of SVR (M2). After exclusion criteria application, 16 patients included were once more evaluated in M3 (like M1) and classified into regressors (R) or non-regressors (NR), decrease or not >= 25% stiffness, respectively. The results from ROC curve, machine learning (ML) and linear discriminant analysis showed that TCD4 + lymphocytes (absolute) are the most important biomarkers for the prediction of the regression (AUC = 0.90). NR patients presented levels less than R of liver stiffness since baseline, whereas NK cells were increased in NR. Therefore, it was concluded that there is a difference in the profile of circulating immune cells in R and NR, thus allowing the development of a predictive model of regression of liver stiffness after SVR. These findings should be validated in greater numbers of patients.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据