Macpherson, Iain;
Abeysekera, Kushala WM;
Harris, Rebecca;
Mansour, Dina;
McPherson, Stuart;
Rowe, Ian;
Rosenberg, William;
... Specialist Interest Group, in the Early Detection of Liver Disease; + view all
(2022)
Identification of liver disease: why and how.
Frontline Gastroenterology
, 13
(5)
pp. 367-373.
10.1136/flgastro-2021-101833.
Preview |
Text
Rosenberg_Identification of liver disease_AAM.pdf Download (199kB) | Preview |
Abstract
Mortality from chronic liver disease (CLD) in the UK has increased by over 400% since 1970, driven by alcohol, non-alcoholic fatty liver disease and hepatitis C virus, the natural histories of which can all be improved by early intervention. Patients often present with advanced disease, which would be preventable if diagnosed earlier and lifestyle change opportunities offered. Liver function tests (LFTs) are very commonly measured. Approximately 20% are abnormal, yet the majority are not investigated according to guidelines. However, investigating all abnormal LFTs to identify early liver disease would overwhelm services. Recently, several diagnostic pathways have been implemented across the country; some focus on abnormal LFTs and some on stratifying at-risk populations. This review will collate the evidence on the size of the problem and the challenges it poses. We will discuss the limitations and restrictions within systems that limit the responses available, review the current pathways being evaluated and piloted in the UK, and explore the arguments for and against LFT-based approaches and 'case-finding strategies' in the community diagnosis of liver disease. Furthermore, the role of fibrosis assessment methods (including scoring systems such as Fibrosis-4 (FIB-4) index, the enhanced liver fibrosis test and elastography) within these pathways will also be discussed. In conclusion, this review aims to establish some principles which, if adopted, are likely to improve the diagnosis of advanced liver disease, and identify the areas of contention for further research, in order to establish the most effective community detection models of liver disease.
Archive Staff Only
 |
View Item |
