UCSF EMR Drives RWD for Compelling Multiple Sclerosis Research Results

Mar 29, 2019 | EHR, Electronic Health Records, Electronic Medical Record, EMR, Multiple Sclerosis, Real World Evidence, Real-world data

Multiple Sclerosis

A team of researchers from diverse backgrounds capitalized on a fantastic opportunity in a world -class institution: University of California, San Francisco (UCSF). The team, representing complimentary skill sets, backgrounds and nationalities,  leveraged UCSF’s electronic medical records (EMR) system to produce real-world cohorts for multiple sclerosis (MS) research. TrialSite News summarizes the results here with link to the actual study published in Multiple Sclerosis Journal.

The team’s results contribute to what appears to be a growing momentum within the MS research community to leverage and harness EMRs fro compelling real-world data informing and contributing to MS research. The team identified 4,142 MS patients in the EMR and compared to 337 patients in a research cohort. Thereafter, they classified patients into 4 groups including:

  • Well-defined MS Group
  • Probably MS Group A
    Probably MS Group B
  • Probably MS Group C

Algorithms were implemented to identify MS patients from UCSF’s EMR, de-identify the data and extract clinical variables.  This EMR-extracted data was compared to research cohort data in a subset of patients.  For example, to produce MS clinical variables, The UCSF researchers extracted 25,260 values from the EMR visit notes’ text. These MS clinical variables included:

  • Expanded Disability Status Scale (EDSS) score
  • Timed-25 foot walk (T25FW)
  • MS subtype

o   Relapsing remitting (RR)

o   Secondary progressive (SP)

o   Primary progressive

  • Disease onset

As reflected in the Sage Journals abstract the team was able to not only identify 4,142 MS patients by harnessing the data within the UCSF EMR but also were able to extract select clinical data with good accuracy—a key point.  Moreover, by utilizing both the EMR and research values they evidenced solid concordance for Expanded Disability Status Scale (EDSS), timed-25-foot walk, and subtype.  The team replicated multiple expected MS epidemiological features from MS natural history including higher EDSS for progressive versus relapsing-remitting patients and for male vs. female patients as well as increased EDSS with age at exam and disease duration.  The UCSF-originated study is yet another example of significant real-world cohorts algorithmically extracted from electronic health records/electronic medical records for MS clinical research.

Lead Research/Investigators

Vincent Damotte (at the time of the study Vincent was a researcher with UCSF and is now with Institut Pasteur de Lille

Antoine Lizee, Dept. of Neurology, UCSF

Matthew Tremblay (at time of research Fellow UCSF now with RWJ Barnabas Health

Alisha Agrawal


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