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3D-MRE for Assessing Cirrhosis, Advanced Chronic Liver Disease and Portal Hypertension

C

China Medical University

Status

Enrolling

Conditions

Portal Hypertension
Cirrhosis, Liver
Advanced Chronic Liver Disease

Treatments

Device: 3D-MRE
Device: HVPG

Study type

Observational

Funder types

Other

Identifiers

NCT05475015
CHESS2206

Details and patient eligibility

About

How to construct a novel, non-invasive, accurate, and convenient method to achieve prediction of hepatic venous pressure gradient (HVPG) is an important general problem in the management of portal hypertension in cirrhosis or advanced chronic liver disease. We plan to investigate the ability of three demensional-magnetic resonance elastography (3D-MRE) to establish a risk stratification system and perform tailored management for portal hypertension in cirrhosis or advanced chronic liver disease.

Full description

China suffers the heaviest burden of liver disease in the world. The number of chronic liver disease is more than 400 million. Either viral-related hepatitis, alcoholic hepatitis, or metabolic-related fatty hepatitis, etc. may progress to cirrhosis, which greatly threatens public health. Portal hypertension is a critical risk factor that correlates with clinical prognosis of patients with cirrhosis. According to the Consensus on clinical application of hepatic venous pressure gradient in China (2018), hepatic venous pressure gradient (HVPG) greater than 10,12,16,20 mmHg correspondingly predicts different outcomes of patients with cirrhosis portal hypertension. It is of great significance to establish a risk stratification system and perform tailored management for portal hypertension in cirrhosis. As a universal gold standard for diagnosing and monitoring portal hypertension, HVPG remains limitation for clinical application due to its invasiveness. How to construct a novel, non-invasive, accurate, and convenient method to achieve prediction of HVPG is an important general problem in the management of portal hypertension in cirrhosis and advanced chronic liver disease.

Multiparametric three-dimensional (3D) MR elastography allows for basic viscoelastic modeling of tissue, partitioning the complex shear modulus into elastic components (eg, storage modulus) and viscous components (eg, loss modulus and damping ratio [DR]). However, these mechanical properties of tissue measured with use of 3D MR elastography have yet to be investigated in cirrhosis to identify specific hepatic pathophysiologic interrelations. We hypothesize that these mechanical properties might be valid presumptive surrogates of cirrhosis and portal hypertension, perhaps capable of supplanting liver biopsy or other invasive diagnostic interventions. This project aims to investigate the ability of three demensional-magnetic resonance elastography (3D-MRE) to establish a risk stratification system and perform tailored management for portal hypertension in cirrhosis and advanced chronic liver disease.

Enrollment

100 estimated patients

Sex

All

Ages

18 to 75 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  1. age > 18 years old
  2. confirmed cirrhosis or ACLD (advanced chronic liver disease) (laboratory, imaging and clinical symptoms)
  3. with 3D-MRE within 1 month prior to HVPG measurement
  4. written informed consent

Exclusion criteria

  1. any previous liver or spleen surgery
  2. liver cancer; chronic acute liver failure
  3. acute portal hypertension
  4. unreliable HVPG or 3D-MRE results due to technical reasons
  5. with liver interventional therapy between HVPG and 3D-MRE

Trial design

100 participants in 2 patient groups

Training cohort
Description:
Training cohort was set to develop the novel non-invasive model for virtual HVPG
Treatment:
Device: HVPG
Device: 3D-MRE
Validation cohort
Description:
Validation cohort was set to validate the novel non-invasive model for virtual HVPG in different people in same environments
Treatment:
Device: HVPG
Device: 3D-MRE

Trial contacts and locations

1

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Central trial contact

Yifei Huang, M.D.; Yu Shi, Prof.

Data sourced from clinicaltrials.gov

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