Deep hierarchical subtyping of multi-organ systemic sclerosis trajectories - a EUSTAR study

Abstract

Systemic sclerosis (SSc) is a chronic autoimmune disease with multi-organ involvement. Historically, SSc classification has focused on the type of skin involvement (limited versus diffuse); however, a growing evidence of organ-specific variability suggests the presence of more than two distinct subtypes. We propose a semi-supervised generative deep learning framework leveraging expert-driven definitions of organ-specific involvement and severity. We model SSc disease trajectories in the European Scleroderma Trials and Research (EUSTAR) database, containing 14,000 patients across 67,000 medical visits, and identify clinically meaningful subtypes to enhance patient stratification and prognosis. We systematically evaluate the model’s predictive accuracy, robustness to missing data, and clinical interpretability. We identified five patient clusters, separating patients based on the degree of organ involvement. Notably, a subset with limited skin involvement still showed high risks of lung and heart complications, underscoring the importance of data-driven methods and multi-organ models to complement established insights from clinical practice.