ArticleMultiple sclerosis (Houndmills, Basingstoke, England)2024
Multiple sclerosis subgroups: Data-driven clusters based on patient-reported outcomes and a large clinical sample.
Article in Multiple sclerosis (Houndmills, Basingstoke, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Phenotypic Diversity in Multiple Sclerosis Can Be Represented by Four Additive Symptom Modules.Brain sciences · 2026Article
- Improving Survival Prediction of Head and Neck Cancer Patients by Modeling Long-term Symptom Burden.Proceedings. IEEE International Conference on Healthcare Informatics · 2026Article
- Confirmed Worsening of Health-Related Quality of Life to Monitor Long-Term MS Disease Progression: A Longitudinal Analysis From the Swiss Multiple Sclerosis Registry.European journal of neurology · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundWhile standard clinical assessments provide great value for people with multiple sclerosis (PwMS), they are limited in their ability to characterize patient perspectives and individual-level symptom heterogeneity.
objectivesTo identify PwMS subgroups based on patient-reported outcomes (PROs) of physical, cognitive, and emotional symptoms. We also sought to connect PRO-based subgroups with demographic variables, functional impairment, hypertension and smoking status, traditional qualitative multiple sclerosis (MS) symptom groupings, and neuroperformance measurements.
methodsUsing a cross-sectional design, we applied latent profile analysis (LPA) to a large database of PROs; analytic sample
resultsWe identified nine distinct MS subtypes based on PRO patterns. The subtypes were primarily categorized into low, moderate, and high mobility impairment clusters. Approximately 70% of participants were classified in a low mobility impairment group, 10% in a moderate mobility impairment group, and 20% in a high mobility impairment group. Within these subgroups, several unexpected patterns were observed, such as high mobility impairment clusters reporting low non-mobility impairment.
conclusionsThe present study highlights an opportunity to advance precision medicine approaches in MS. Combining PROs with data-driven methodology allows for a cost-effective and personalized characterization of symptom presentations. that can inform clinical practice and future research designs.
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