RT Journal Article
SR Electronic
T1 Identification of liver disease: why and how
JF Frontline Gastroenterology
JO Frontline Gastroenterol
FD BMJ Publishing Group Ltd
SP 367
OP 373
DO 10.1136/flgastro-2021-101833
VO 13
IS 5
A1 Iain Macpherson
A1 Kushala W M Abeysekera
A1 Rebecca Harris
A1 Dina Mansour
A1 Stuart McPherson
A1 Ian Rowe
A1 William Rosenberg
A1 John F Dillon
A1 Andrew Yeoman
A1 ,
YR 2022
UL http://fg.bmj.com/content/13/5/367.abstract
AB 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.