Non-invasive Methods for Predicting Decompensation of Chronic Liver Disease
NCT06741904 · Status: ENROLLING_BY_INVITATION · Type: OBSERVATIONAL · Enrollment: 2334
Last updated 2024-12-19
Summary
Preliminary studies have suggested that non-invasive methods can not only be applied to CSPH but also to predict the risk of decompensation in cirrhosis. However, there is a lack of clinical evidence, and more research is needed to provide such evidence. Especially in China, where the etiology of cirrhosis is prevalent, there is a large population of patients with hepatitis B cirrhosis undergoing antiviral treatment. Exploring the value of non-invasive methods in predicting decompensation events in these patients can not only expand the clinical application of non-invasive methods but also provide effective non-invasive screening and management strategies for patients with cirrhosis at different risk levels.
Primary Objective: The main purpose of this study is to evaluate the predictive effectiveness of non-invasive methods (based on liver and spleen stiffness) for the occurrence of decompensation in chronic liver disease (CLD).
Secondary Objectives: To establish different predictive models for the occurrence of decompensation in CLD and to assess their predictive effectiveness as non-invasive methods for decompensation in CLD.
Approximately 2334 individuals will participate in this study at 17 different health care Setting.The study will last for 4 years, including 1 year of enrollment and 3 years of follow-up. Patients will be seen at 6-month intervals, and all examination results of patients as well as decompensation events, liver cancer, and death will be recorded.
Conditions
- Chronic Liver Disease and Cirrhosis
Sponsors & Collaborators
-
Nanfang Hospital, Southern Medical University
lead OTHER
Eligibility
- Min Age
- 18 Years
- Max Age
- 80 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2024-12-16
- Primary Completion
- 2027-12-31
- Completion
- 2029-12-31
Countries
- China
Study Locations
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