Cognitive impairment, physical disability and progressive disease are common but understudied clinical outcomes that substantially impact employment and overall quality of life for individuals with multiple sclerosis (MS). We have recently developed and validated an assisted, web-based tool (ICLIC-MS) for systematic and longitudinal clinical outcomes data collection of MS-validated cognitive function measures, physical disability and progressive disease measures that are not reliably captured in the electronic health record (EHR). In response to FOA# PA-17-010, Use of Technology to Enhance Patient Outcomes and Prevent Illness, our team proposes the study of MS outcomes in a large, multi-ethnic population representative sample of more than 3,000 female and male MS cases from the Kaiser Permanente Northern California Health Plan Membership. We will integrate other EHR data such as important comorbid conditions, use of disease modifying therapy, MRI reports, as well as quality of life measures and employment histories. Our goals include: 1) comprehensively characterizing clinical outcomes in a large MS patient cohort; 2) developing and utilizing an integrated MS health report to enhance patient care; and 3) establishing a resource for clinical outcomes research in MS that also includes whole genomic and environmental exposure data. Findings from our proposed study represent an extraordinary opportunity to facilitate effective long-term management of MS, accelerate progress in the understanding of disease pathogenesis, predict patient trajectories and inform prevention strategies. We have assembled a team with strong expertise in clinical neurology/MS neurology, advanced epidemiologic methods, EHR structure and clinical care within a health maintenance organization, human genomics, biostatistics, and big data approaches.

Public Health Relevance

Our team has developed and validated an assisted web-based interface for longitudinal clinical data collection of cognitive function measures, physical disability and depression measures that are not reliably captured in the electronic health record (EHR) of multiple sclerosis (MS) patients. We will integrate several sources of patient data including genomic, clinical and EHR for 3,000 individuals to facilitate research and improve clinical care.

Agency
National Institute of Health (NIH)
Institute
National Institute of Nursing Research (NINR)
Type
Research Project (R01)
Project #
5R01NR017431-02
Application #
9763663
Study Section
Nursing and Related Clinical Sciences Study Section (NRCS)
Program Officer
Diana, Augusto
Project Start
2018-08-14
Project End
2023-05-31
Budget Start
2019-06-12
Budget End
2020-05-31
Support Year
2
Fiscal Year
2019
Total Cost
Indirect Cost
Name
University of California Berkeley
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
124726725
City
Berkeley
State
CA
Country
United States
Zip Code
94704