# Leveraging electronic health records to optimize treatment selection and response in multiple sclerosis

> **NIH NIH R01** · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · 2024 · $595,341

## Abstract

PROJECT SUMMARY AND ABSTRACT
The rapid expansion of approved multiple sclerosis (MS) disease-modifying therapies (DMTs)
and the diverse individual variation in treatment response contribute to the critical unmet need
for individually tailored treatment strategy for the nearly 3 million persons with multiple sclerosis
(pwMS) worldwide. The shift towards a precision medicine approach to guide treatment
selection based on individual profiles will improve patient outcome by ensuring prompt initiation
of effective DMTs while avoiding ineffective DMTs. To fill the knowledge gaps due to the
absence of randomized clinical trial evidence and to advance precision medicine for pwMS, it is
crucial to harness available clinical data and develop approaches deployable at the point of
care. Electronic health records (EHR) data contain a wealth of longitudinal real-world clinical
information and provide a complementary platform for clinical discovery. Building on our prior
research efforts, the proposed study has the overall goal to optimize DMT selection and patient
outcomes in pwMS using EHR data. We will use EHR data from two academic healthcare
systems, both ideally positioned as they contain longitudinal clinical information of thousands of
pwMS and hold crucial linkage to MS research registries that provide the ground truth. For
additional validation, we will use integrated claims and EHR data from a large population of
commercially insured pwMS. Aim 1: Compare relapse outcomes across DMTs. We will test
the hypothesis that confounder correction using full EHR features yields more robust and
consistent results in DMT effectiveness comparison analysis than expert-selected covariates.
We will test the generalizability by using a transfer learning approach. Aim 2: Identify patient
clusters based on DMT prescription sequences over time. We will test the hypothesis that
DMT prescription sequences inform differential patient clusters and MS outcomes. We will apply
a covariate-adjusted mixture Markov Model. Aim 3: Identify optimal DMT sequences that
predict favorable treatment response. We will test the hypothesis that optimized DMT
prescription sequence(s) through reinforcement learning could improve MS outcomes (i.e.,
relapse rate, patient-reported outcomes). This research will close knowledge gaps due to
absent randomized clinical trial evidence and limited real-world evidence to guide optimal MS
treatment selection. It will bring precision medicine closer to pwMS by developing clinically
deployable strategies to optimize treatment selection. This project is consistent with the mission
of the NINDS to reduce the burden of neurological diseases such as MS.

## Key facts

- **NIH application ID:** 10756127
- **Project number:** 5R01NS098023-07
- **Recipient organization:** UNIVERSITY OF PITTSBURGH AT PITTSBURGH
- **Principal Investigator:** Zongqi Xia
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $595,341
- **Award type:** 5
- **Project period:** 2016-09-30 → 2027-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10756127

## Citation

> US National Institutes of Health, RePORTER application 10756127, Leveraging electronic health records to optimize treatment selection and response in multiple sclerosis (5R01NS098023-07). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10756127. Licensed CC0.

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