Predictive modeling in multiple sclerosis: using real-world data to inform practice, policy, and research

Multiple sclerosis (MS), a progressive disease of the brain and spinal cord, affects millions globally, including 93,500 Canadians. With one of the highest existing global MS rates, the number of Canadians living with MS is expected to increase to 133,600 by 2031 and the economic burden to reach $2 billion by the same date.

Treatments for MS are available to help manage and reduce the number episodes of increased disability, and control symptoms. But, while there have been many studies on independent risk factors for patient health outcomes, no study to date has attempted to combine patient, disease, and treatment-related factors to better predict treatment outcomes.

This study will link provincial health care records data with clinical data, to develop predictive models on MS-related outcomes, and make it available on a website to guide better treatment decisions. Better patient care will ultimately facilitate the cost-effective use of health care resources.

Faculty Supervisor:

Larry Lynd

Student:

Partner:

Providence Health Care

Discipline:

Life Sciences

Sector:

Health and Related Sciences & Technology; Professional, scientific and technical services

University:

The University of British Columbia

Program: