Probabilistic Clustering Using Multivariate Growth Mixture Model in Clinical Settings-A Scleroderma Example.

ABSTRACT:

Background

Scleroderma (systemic sclerosis; SSc) is a chronic autoimmune disease known for wide heterogeneity in patients' disease progression in multiple organ systems. Our goal is to guide clinical care by real-time classification of patients into clinically interpretable subpopulations based on their baseline characteristics and the temporal patterns of their disease progression.

Methods

A Bayesian multivariate growth mixture model was fit to identify subgroups of patients from the Johns Hopkins Scleroderma Center Research Registry who share similar lung function trajectories. We jointly modeled forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO) as pulmonary outcomes for 289 patients with SSc and anti-topoisomerase 1 antibodies and developed a f

SUBMITTER: Kim JS 

PROVIDER: S-EPMC12904757 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature