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Multiple sclerosis subgroups: Data-driven clusters based on patient-reported outcomes and a large clinical sample.

Alessandro S De Nadai¹, Ryan J Zamora¹, Alyse Finch¹

¹McLean Hospital, Harvard Medical School, Belmont, MA, USA.

Multiple Sclerosis (Houndmills, Basingstoke, England)

|October 18, 2024

Summary

This summary is machine-generated.

This study identified nine distinct multiple sclerosis (MS) subtypes using patient-reported outcomes, revealing varied symptom patterns and enabling personalized medicine approaches for MS care.

Keywords:

Multiple sclerosis (MS)health disparities latent profile analysis (LPA)patient-reported outcomes (PROs)

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Area of Science:

Neurology
Clinical Research
Data Science

Background:

Standard clinical assessments for multiple sclerosis (MS) have limitations in capturing patient experiences and symptom diversity.
Patient perspectives and individual symptom variations are crucial for comprehensive MS care.

Purpose of the Study:

To identify subgroups of people with MS (PwMS) based on patient-reported outcomes (PROs) for physical, cognitive, and emotional symptoms.
To correlate these PRO-based subgroups with demographic, clinical, and neuroperformance data.

Main Methods:

Employed latent profile analysis (LPA) on a large dataset of PROs from 6,619 participants.
Utilized a cross-sectional study design to analyze symptom patterns.

Main Results:

Identified nine distinct MS subtypes characterized by PRO patterns.
Subtypes were primarily grouped into low, moderate, and high mobility impairment categories.
Observed unexpected patterns, including high mobility impairment in some subgroups with low non-mobility symptoms.

Conclusions:

Highlights the potential for precision medicine in MS by integrating PROs and data-driven methods.
PRO-based subgrouping offers a cost-effective, personalized approach to characterizing MS symptoms.
Findings can inform clinical practice and guide future research in multiple sclerosis.