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Intelligent Liver Function Testing (iLFT)

Published on 8 June 2021

A tool for early diagnosis and treatment of liver disease

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Liver disease is the 5th largest cause of death in the UK and the only major disease area with an increasing death rate for those under 65 years of age. This growing epidemic of advanced liver disease could be mitigated by early detection and management. Liver function is routinely investigated by liver blood tests (LFTs) with approximately 23.5m such tests carried out annually by GPs in the UK. However, further investigation of the approximately 20% abnormal results of LFT tests is often incomplete, missing opportunities to save lives.

The Intelligent Liver Function Test (iLFT) implements an intelligent prediction algorithm which standardises the application and investigation of LFT results. With the addition of referral recommendations and management plans, iLFT provides highly cost-effective automated pathways for the investigation of abnormal liver function tests.

Professor John Dillon, consultant gastroenterologist and hepatologist, and Dr Ellie Dow, consultant in biochemical medicine, worked with colleagues from the University of Dundee and NHS Tayside to develop minimum diagnostic criteria of liver disease, based on clinical information. Combined with repurposed biomarkers to identify patients with low-risk liver complications, this created a system for automated diagnosis which provides GPs with a rapid and reliable triage tool for liver abnormalities. Laboratory information management systems were programmed to redirect samples and integrate resulting clinical data using the iLFT algorithm, which generates one of 32 possible diagnostic outcomes and associated management plans.

“By applying variables to the existing IT systems in the lab, we were able to develop a system that detects the early warning signs of liver disease”

Professor John Dillon

Prof John Dillon: “By applying variables to the existing IT systems in the lab, we were able to develop a system that detects the early warning signs of liver disease and which can then give GPs the tools they need to make a solid diagnosis and begin treatment plans. More importantly, our modification allows us to immediately differentiate between alcoholic or non-alcoholic fatty liver disease and the more rare diseases such as autoimmune liver diseases, Hepatitis C or metabolic diseases, meaning those who need immediate assistance receive it faster.”

iLFT increases diagnosis of liver disease by 43%, with diagnostic accuracy over 90% and enables earlier identification of treatable liver disease. It makes standard care easier and is estimated to deliver cost saving to the NHS of £3,216 per patient lifetime compared to current ideal investigation. A single sample provides results which would previously have required up to six separate bloods which reduces GP contacts by an estimated 60%.

The implementation of iLFT as standard care in NHS Tayside has contributed to the effective elimination of hepatitis C in Tayside. The Lancet Commission for Liver Disease has recommended iLFT for implementation to reduce the burden of liver disease and it is being adopted across the UK.

“The journey to date has been truly transformational, empowering primary care to have the confidence in managing low risk liver disease, expediting care for those with significant liver disease who truly need secondary care input and undoubtedly shortening the timescales in the assessment and management of liver disease across the heath economy.”

Dr Denis Burke MD FRCP FRCPEd, North Cumbria Integrated Care NHS Foundation Trust

The Dillon team’s achievements in integrated clinical care were rewarded with the UNIVANTS Healthcare Excellence Award 2019, which recognises teams who collaborate across disciplines to transform healthcare delivery and patient lives as well as innovation awards from the Royal College of Physicians, the British Society of Gastroenterology and the Royal College of Pathology.

Enquiries

For further information or to engage with our research, please contact:

research@dundee.ac.uk

Story category Research