Deep hierarchical subtyping of multi-organ systemic sclerosis trajectories - a EUSTAR study
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Deep hierarchical subtyping of multi-organ systemic sclerosis trajectories - a EUSTAR study
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30226 - Rheumatology
Result continuities
Project
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Continuities
V - Vyzkumna aktivita podporovana z jinych verejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
NPJ Digit Med .
ISSN
2398-6352
e-ISSN
2398-6352
Volume of the periodical
8
Issue of the periodical within the volume
563
Country of publishing house
GB - UNITED KINGDOM
Number of pages
10
Pages from-to
1-10
UT code for WoS article
001693798300001
EID of the result in the Scopus database
2-s2.0-105016567420