Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 12:17 AM
Ignite Modification Date:
2025-12-25 @ 12:17 AM
NCT ID:
NCT04873258
Brief Summary:
A generic screening study to establish structural and/or functional baselines of specific organs.
Detailed Description:
Fatty liver disease is a common condition (25% of the population) which can lead to liver inflammation, liver scarring and even liver cancer. Clinical trials are often performed in healthy volunteers, who may have underlying fatty liver without knowledge of it. In clinical trials fatty liver can both mean volunteers have abnormal liver tests, preventing them joining the trial, as well as more likely to have a possible liver drug reaction, causing volunteers to withdraw from a clinical trial of a new drug.
The principal objective of the study is to develop a clinical scoring tool that can accurately predict fatty liver disease in study volunteers, without the need for invasive tests (such as a tissue biopsy).
We aim to recruit initially 2000 volunteers to this study, both healthy volunteers and patients with known MASLD.
Volunteers will attend the unit to undergo all assessments on one day. Once consent is given with a study research physician, bloods will be taken and body measurements made (including BMI, weight, waist circumference). A full medical history and physical examination will then be performed by the research physician.
Bioimpedance body composition analysis will then be performed on an ACUNIQ device. Finally ultrasound of the liver and fibroscan will be performed. Once all assessments are complete the study volunteer will be discharged from the unit.
Once all results are finalised, analysis will be performed on all the data to create a clinical score to predict the presence of MASLD, both with statistical and machine learning methods.
Study:
NCT04873258
Study Brief:
Protocol Section:
NCT04873258