Machine learning in primary biliary cholangitis: A novel approach for risk stratification.
Alessio Gerussi1,2, Damiano Verda3, Davide Paolo Bernasconi4
1Division of Gastroenterology, Center for Autoimmune Liver Diseases, Department of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.
Summary
Machine learning identified four patient groups in primary biliary cholangitis (PBC), aiding disease sub-phenotyping and risk stratification. Albumin levels within the normal range are key indicators for prognosis and treatment goals.
Area of Science:
- Hepatology
- Medical Informatics
- Biostatistics
Background:
- Machine learning (ML) offers novel approaches for patient prognostication by identifying distinct subgroups.
- Primary biliary cholangitis (PBC) management can benefit from advanced analytical methods for sub-phenotyping and risk stratification.
Purpose of the Study:
- To explore the utility of machine learning (ML) in defining patient subgroups within primary biliary cholangitis (PBC).
- To assess ML's capability for risk stratification in PBC patients.
- To identify novel prognostic markers, including subtle variations in albumin levels.
Main Methods:
- An international PBC patient dataset was utilized, divided into training and validation cohorts.
- Unsupervised machine learning algorithms were applied to identify patient clusters based on clinical features.
- Standard survival analysis was performed alongside ML to compare prognostic accuracy.
Main Results:
- ML identified four distinct patient clusters with varying phenotypes and prognoses.
- Cluster 2 patients showed worse prognosis, differentiated by albumin levels near the normal limit.
- An increase in albumin above 1.2 times the lower limit of normal (LLN), particularly with ursodeoxycholic acid treatment, correlated with improved transplant-free survival.
Conclusions:
- Unsupervised ML successfully delineated four novel prognostic groups in PBC patients.
- Subtle variations in albumin levels within the normal range are significant prognostic indicators.
- Achieving a therapy-induced increase in albumin above 1.2 x LLN should be a therapeutic target in PBC management.
