Genentech, Inc.

Author Of 1 Presentation

Observational Studies Poster Presentation

P0875 - FlywheelMS: The prevalence of multiple sclerosis subtypes in digitized health records (ID 1882)

Speakers
Presentation Number
P0875
Presentation Topic
Observational Studies

Abstract

Background

Data generated from electronic health records (EHRs) offer insight into real-world care of people with multiple sclerosis (MS). Data extracted most readily from EHRs include templated or administrative health information (e.g., MS International Classification of Diseases codes). However, clinical data like disease subtype and characteristics are unlikely to be captured systematically. FlywheelMS is a novel patient-centered study with the aim of digitizing health records of patients with MS and extracting information not readily available in existing EHRs.

Objectives

To evaluate patient characteristics and the prevalence of MS subtypes (i.e., relapsing-remitting MS [RRMS], secondary progressive MS [SPMS], primary progressive MS [PPMS], progressive relapsing MS [PRMS]) in the FlywheelMS cohort and to compare them with existing real-world data sources.

Methods

Adults with MS are recruited across the US via advocacy groups, social media and healthcare professionals. Supervised machine learning with human curation is used to retrieve, digitize and abstract medical records, which are collected as far back as are available and prospectively up to 5 years after enrollment. The most recent non-negated MS subtype from neurology visit records was used as a proxy for the prevalent subtype. Summary statistics were calculated and compared with other MS cohorts.

Results

As of March 1, 2020, 2,389 patients with MS with 24,362 neurology visits across 3,093 neurologists have enrolled in FlywheelMS. Data on MS subtype were available for 973 patients (40.7%); this proportion will increase as abstractions continue. RRMS accounted for 78.9% of patients, followed by SPMS (12%), PPMS (7.3%) and PRMS (1.7%). These findings were comparable to the MSBase Registry (RRMS=76.9%, SPMS=13.0%, PPMS=8.0%, PRMS=2.2%; Kister et al., J Neurol Sci 2012) and NARCOMS Registry (RRMS=65.6%, SPMS=25.1%, PPMS=9.3%; Salter et al., Mult Scler 2018). Mean [SD] age at mention of MS subtype and percent female distribution were as follows: RRMS (46.4 [10.9] years, 80.4%), SPMS (56.4 [9.6] years, 81.2%), PPMS (53.9 [10.7] years, 62.0%), PRMS (mean 53.9 [5.5] years, 82.4%).

Conclusions

The prevalence of MS subtypes in the digitized health records of patients in FlywheelMS was comparable to other real-world data sources. Digitizing and machine-learning guided abstraction of patient healthcare records in MS yields important data about clinical features not readily available in other EHR data sets.

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