# Elucidating symptoms clusters in multiple sclerosis using patient reported outcomes and unsupervised machine learning

> **NIH NIH R56** · CASE WESTERN RESERVE UNIVERSITY · 2021 · $156,251

## Abstract

PROJECT SUMMARY
 Multiple sclerosis (MS) is a chronic disease affecting 900,000 persons in the U.S, and it is a leading cause of
disability among young adults. Persons with MS (PwMS) experience wide-ranging symptoms across multiple domains,
alone or in combination, with varied severity. Some of these symptoms include optic nerve dysfunction and vision problems,
muscle weakness, bladder/bowel dysfunction, tremors, cognitive and emotional problems, and incoordination. The
objectives of the current application are to identify and characterize symptom patterns and clusters in PwMS, which are
aligned with PA-17-462, that states: “multiple sclerosis (is a) … model condition to advance (symptom) cluster research”.
Thus, our analytical framework will inform research in other poly-symptomatic conditions.
 Stakeholders agree that the benefits of patient reported outcomes (PROs) and measures (PROMs) have not reached
their full potential for PwMS. PROs, which provide invaluable insight into the patients’ perspective, are increasingly used
in MS clinical trials and clinical practice as standard clinical measures fail to adequately measure impairment across domains
or lack sensitivity to detect subtle but meaningful change. Aligning with ongoing global MS initiatives, the current proposal
will focus on identifying and characterizing symptom patterns and clusters for PROs; which is also directly aligned with
NOT-OD-20-079, a Notice of Special Interest to stimulate “research to improve the interpretation of PROs at the individual
patient level for use in the clinical practice”. Furthermore, there is an additional incentive to maximize the use and
interpretation of PROs considering the shift to telemedicine service in response to the COVID-19 pandemic.
 We have assembled a multi-disciplinary team of research scientists and clinical experts with access to two
unparalleled data resources (discovery and validation data sets), the 1st being the North American Research Committee on
Multiple Sclerosis (NARCOMS) Registry’s survey data for 21,558 PwMS spanning an average of 8.4 years (0.5 to 14 years)
and 269,468 biannual surveys, and the 2nd being the structured electronic health records (EHRs) for 8,687 PwMS see at the
Mellen Center for MS Treatment and Research (MCMS) at the Cleveland Clinic, spanning an average of 4 years (0.5-8.4
years) and 67,932 visits. In both resources, 11 MS-specific PROMs (MS-PROMs) were longitudinally captured including
measures of mobility, dexterity, vision, fatigue, cognition, bladder/bowel, sensory, spasticity, depression,
tremor/coordination, and pain. We propose the four complementary aims that will: 1. Characterize overall longitudinal
impairment patterns for each 11 MS-PROMs; 2. Identify distinct clusters of Pw MS with similar symptom patterns within
and across functional domains using machine learning approaches; 3. Develop new approaches to assess the strength of
causal inference and identify sources of model prediction errors in un...

## Key facts

- **NIH application ID:** 10440701
- **Project number:** 1R56NR019306-01A1
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Farren B. S. Briggs
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $156,251
- **Award type:** 1
- **Project period:** 2021-08-24 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10440701

## Citation

> US National Institutes of Health, RePORTER application 10440701, Elucidating symptoms clusters in multiple sclerosis using patient reported outcomes and unsupervised machine learning (1R56NR019306-01A1). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10440701. Licensed CC0.

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