LiverTrail

Aim

This PhD project aimed to develop and validate a decision aid for use in primary health care to assess the risk of advanced fibrosis in patients with alcoholic- and non-alcoholic fatty liver disease. The decision aid – LiverTRAIL – was algorithm-based, combining results from routine liver blood tests into multiple diagnostic algorithms, thus utilising computational power for large-scale data aggregation and pattern recognition.

The aim was that LiverTRAIL could transform knowledge from original research data into a rational decision tool for the general practitioner. 

LiverTRAIL sought to unite three main challenges within the study of fatty liver disease:

1. To identify patients with severe fibrosis due to ALD and NAFLD (rule-in).
2. To exclude patients with ALD and NAFLD at very low risk of advanced fibrosis from further diagnostic investigations (rule-out).
3. To monitor patients at high and moderate risk of advanced fibrosis for disease progression and fibrosis improvement, e.g. during anti-fibrotic treatment.

The project hypothesised that through the implementation of LiverTRAIL in the primary sector, it would possible to achieve early detection of patients with asymptomatic, early-stage severe fibrosis and cirrhosis.

Participants

• FLASH – Centre for Liver Research, OUH
• Professor Esmaeil S. Nadimi, Applied AI and Data Science, The Maersk Mc-Kinney Moller Institute, Faculty of Engineering, SDU

Funding

The project was funded by Innovation Fund Denmark.