Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
08 Jul 2024
08 Jul 2024
Historique:
received:
29
08
2023
accepted:
25
06
2024
medline:
9
7
2024
pubmed:
9
7
2024
entrez:
8
7
2024
Statut:
epublish
Résumé
Identifying groups of patients with similar disease progression patterns is key to understand disease heterogeneity, guide clinical decisions and improve patient care. In this paper, we propose a data-driven temporal stratification approach, ClusTric, combining triclustering and hierarchical clustering. The proposed approach enables the discovery of complex disease progression patterns not found by univariate temporal analyses. As a case study, we use Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease with a non-linear and heterogeneous disease progression. In this context, we applied ClusTric to stratify a hospital-based population (Lisbon ALS Clinic dataset) and validate it in a clinical trial population. The results unravelled four clinically relevant disease progression groups: slow progressors, moderate bulbar and spinal progressors, and fast progressors. We compared ClusTric with a state-of-the-art method, showing its effectiveness in capturing the heterogeneity of ALS disease progression in a lower number of clinically relevant progression groups.
Identifiants
pubmed: 38977678
doi: 10.1038/s41467-024-49954-y
pii: 10.1038/s41467-024-49954-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5717Subventions
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 European Institute of Innovation and Technology (H2020 The European Institute of Innovation and Technology)
ID : 101017598
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 European Institute of Innovation and Technology (H2020 The European Institute of Innovation and Technology)
ID : 101017598
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 European Institute of Innovation and Technology (H2020 The European Institute of Innovation and Technology)
ID : 101017598
Organisme : EC | EU Framework Programme for Research and Innovation H2020 | H2020 European Institute of Innovation and Technology (H2020 The European Institute of Innovation and Technology)
ID : 101017598
Informations de copyright
© 2024. The Author(s).
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