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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023728%3A_____%2F25%3AN0000023" target="_blank" >RIV/00023728:_____/25:N0000023 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1038/s41746-025-01962-y" target="_blank" >https://doi.org/10.1038/s41746-025-01962-y</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41746-025-01962-y" target="_blank" >10.1038/s41746-025-01962-y</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30226 - Rheumatology

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

NPJ Digit Med .

ISSN

2398-6352

e-ISSN

2398-6352

Svazek periodika

8

Číslo periodika v rámci svazku

563

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

10

Strana od-do

1-10

Kód UT WoS článku

001693798300001

EID výsledku v databázi Scopus

2-s2.0-105016567420