Latent class mixed modelling for phenotypic stratification of primary biliary cholangitis patients on first line treatment

• Main Authors: Mulcahy, Victoria, Rouanet, Anais, Gerussi, Alessio, Duckworth, Adam, Flack, Steve, Carbone, Marco, Tom, Brian, Mells, George
• Format: Journal Article
• Language: English
• Published: 20-03-2022
• Subjects: Statistics - Applications
• DOI: 10.48550/arxiv.2203.10508

In patients with primary biliary cholangitis (PBC), the serum liver
biochemistry measured during treatment with ursodeoxycholic acid (the UDCA
response) accurately predicts long-term outcome. In this study we sought to use
liver biochemistry, and in particular alkaline phosphatase (ALP), as a
surrogate marker of disease activity, for phenotypic stratification in PBC
using a computational modelling approach. Our aim here was to identify distinct
disease subgroups of patients with distinct disease trajectories. Methods: We
used longitudinal ALP results from 1,601 PBC patients on first line treatment
with UDCA, and applied latent class mixed modelling (LCMM), to identify
distinct phenotypic subgroups, each with distinct disease trajectories, and
risks of end stage liver disease (ESLD). Results: We identified four well
discriminated phenotypic subgroups within our PBC cohort, each with distinct
disease trajectories.