Harnessing electronic medical records to advance research on multiple sclerosis.

Multiple sclerosis (Houndmills, Basingstoke, England)

2018

https://researcherprofiles.org/profile/1216739

29310490

Damotte V, Lizée A, Tremblay M, Agrawal A, Khankhanian P, Santaniello A, Gomez R, Lincoln R, Tang W, Chen T, Lee N, Villoslada P, Hollenbach JA, Bevan CD, Graves J, Bove R, Goodin DS, Green AJ, Baranzini SE, Cree BA, Henry RG, Hauser SL, Gelfand JM, Gourraud PA

Abstract

BACKGROUND

Electronic medical records (EMR) data are increasingly used in research, but no studies have yet evaluated similarity between EMR and research-quality data and between characteristics of an EMR multiple sclerosis (MS) population and known natural MS history.

OBJECTIVES

To (1) identify MS patients in an EMR system and extract clinical data, (2) compare EMR-extracted data with gold-standard research data, and (3) compare EMR MS population characteristics to expected MS natural history.

METHODS

Algorithms were implemented to identify MS patients from the University of California San Francisco EMR, de-identify the data and extract clinical variables. EMR-extracted data were compared to research cohort data in a subset of patients.

RESULTS

We identified 4142 MS patients via search of the EMR and extracted their clinical data with good accuracy. EMR and research values showed good concordance for Expanded Disability Status Scale (EDSS), timed-25-foot walk, and subtype. We replicated several expected MS epidemiological features from MS natural history including higher EDSS for progressive versus relapsing-remitting patients and for male versus female patients and increased EDSS with age at examination and disease duration.

CONCLUSION

Large real-world cohorts algorithmically extracted from the EMR can expand opportunities for MS clinical research.

Journal Issue

Volume 25 of Issue 3