Evaluation of hyaluronic acid and type III procollagen peptide as predictors for treatment response to direct-acting antivirals

评估透明质酸和 III 型前胶原肽作为直接抗病毒药物治疗反应预测因子的价值

阅读:3

Abstract

BACKGROUND: Treatment response to direct-acting antivirals (DAAs) is a challenging issue and the identification of non-responders patients is very important. AIM: To evaluate the relation between baseline serum levels of hyaluronic acid (HA) and type III procollagen N-peptide (PIIINP) with direct-acting antivirals treatment failure in Egyptian patients with chronic hepatitis C. METHODS: Hepatitis C patients (responders and non-responders to sofosbuvir/daclatasvir) were tested for HA and PIIINP using sensitive chemiluminescent immunoassay. RESULTS: There were distinctly higher PIIINP (P = 0.0003) and HA (P < 0.0001) levels in non-responders than responders patients with a good ability for distinguishing non-responders from patients with sustained virological response (area under the curve = 0.766 for HA and 0.684 for PIIINP). Logistic regression analysis revealed that the HA × PIIINP is the model with the highest predictive ability (area under the curve = 0.809). Diagnostic performances were superior to each marker alone with good sensitivity (74.7%), specificity (74%), positive predictive (68.3%), negative predictive values (79.6%) and accuracy (74.3%). The multiplication of HA × PIIINP is correlated significantly (P < 0.05) with elevated liver enzymes (r = 0.212), decreased albumin (r = -0.26), elevated aspartate aminotransferase-platelet ratio index (r = 0.223) and elevated fibrosis-4 score (r = 0.216) scores. CONCLUSION: These findings suggested the remarkable role of fibrogensis markers HA and PIIINP in the prediction of hepatitis C virus DAAs treatment response. Multiplying HA with PIIINP values increase the sensitivity to detect treatment success and thus may aim to improve treatment duration and the disease control.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。