ArticleNature communications2024
Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed.
- Data-Driven SuStaIn Model of Disability Progression in Amyotrophic Lateral Sclerosis.Annals of clinical and translational neurology · 2026Article
- Decomposing heterogeneity in disease progression speeds and pathways.NPJ digital medicine · 2026Article
- Predicting amyotrophic lateral sclerosis stage based on multi-parameter ultrasound: development and validation of an interpretable machine learning model.BMC medicine · 2026Article
- Stable speech BCI performance during slow progression of ALS: A longitudinal ECoG study.Research square · 2026Article
- Temporal phenotyping and prognostic stratification of patients with sepsis through longitudinal clustering.BioData mining · 2025Article
- A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis.PloS one · 2025Article
- Reliable monitoring of respiratory function with home spirometry in people living with amyotrophic lateral sclerosis.Frontiers in neurology · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
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.
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Registered trials
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