FIB-4 as a predictor of advanced liver diseases
Project Details
The FIB-4 index is a widely recognised marker for liver fibrosis and research suggests it could potentially be used as a risk indicator for other serious liver conditions, including liver cancer. To establish a clinically relevant cut-off for identifying patients at risk of developing severe liver disease, we compared FIB-4 thresholds based on their optimal predictive value with those currently recommended to general practitioners (1.3 and 2.67). This comparison was also conducted within a sub-group of patients diagnosed with diabetes to determine if a different FIB-4 cut-off was more appropriate for this high-risk population. Additionally, further analyses explored the risk profiles of various age groups and how the predictive accuracy of FIB-4 changed with age.
Patron ID: PAT1032_8
Project Lead: Dr Sibel Saya
Research Outcomes
Liver disease encompasses a wide range of conditions that impact liver function and contribute significantly to global morbidity and mortality. Its epidemiology is complex, varying across different regions and populations due to genetic, environmental, and lifestyle factors. The FIB-4 (Fibrosis-4) score was originally developed as a non-invasive tool to estimate liver fibrosis, but its potential use in identifying other liver diseases is increasingly being explored in both clinical practice and research. This feasibility study examines whether alternative FIB-4 cut-offs could better identify patients at risk of developing severe liver disease.
Our data exploration suggests that FIB-4 scores differ significantly between patients with and without severe liver disease. Additionally, the PATRON dataset contains sufficient pathology data to calculate FIB-4 scores for a large cohort, allowing for stratification by different liver diseases. The findings also support the application of various statistical methods to determine optimal FIB-4 cut-offs and validate them across separate cohorts.
This feasibility study has also laid the foundation for a new collaboration with an external company interested in developing risk prediction models for multiple liver diseases using FIB-4. This partnership aims to further refine predictive tools and enhance early detection strategies for liver disease in diverse patient populations.