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

TL;DR

This study uses latent class mixed modelling on longitudinal ALP data to identify four distinct phenotypic subgroups of PBC patients, which have different disease trajectories and risks of end-stage liver disease.

Contribution

It introduces a novel application of LCMM to stratify PBC patients based on biochemical trajectories, enhancing understanding of disease heterogeneity.

Findings

Four distinct phenotypic subgroups identified

Each subgroup exhibits unique disease progression patterns

Subgroups differ in risk of end-stage liver disease

Abstract

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.