Can we predict the progression of multiple sclerosis through administrative data analysis?

A woman using crutches with a wheelchair in the background

Multiple sclerosis (MS) is a progressive, immune-mediated disease of the central nervous system which is incurable and often affects people during the most productive years of their lives, reducing life expectancy and leading to significant disability. Canada has one of the highest rates of MS in the world, and its prevalence varies across the country; in 2011 there were approximately 93,500 British Columbians living with MS, and this number is expected to increase to 133,600 in 2031. While early diagnosis and treatment is paramount in managing MS, there are still numerous issues including delayed diagnosis, no effective treatment for progressive MS, an urgent need to better understand how MS progresses, and what affects a person's disability progression or treatment response.

Drs. Larry Lynd and Jacquelyn Cragg, Professors in the Faculty of Pharmaceutical Sciences at the University of British Columbia, are leading a study combining retrospective administrative data with prospectively collected clinical data to generate comprehensive models to predict MS progression, relapse, treatment response, non-adherence, and cost. The aims are to identify epidemiological and clinical patient factors associated with disability progression, treatment response, and the economic burden of disease.

The research will construct a cohort of MS patients spanning a 20-year time-period from January 1, 2001 to December 31, 2020. PopData will link nine data sets to the BC MS Clinical Database, an external data set comprising clinical, demographic, treatment and test information from approximately 15,000 BC MS patients.

“Using these data, we will develop and validate a series of predictive models for MS to facilitate the prediction of both individual and strata-specific health outcomes using fixed and variable patient, treatment, disease, and health system-related factors,” says Professor Lynd. “We will look at the impact of any possible model predictors, such as demographic factors, type/severity of MS, disease history, comorbidities and functional status.”

The models will evaluate the following outcomes of interest: disease progression, relapse, treatment failure or success, treatment discontinuation or non-adherence, and health care resource use. The models generated by this research will form the foundation for an MS research platform integrating real-world data with disease modeling, and the models generated will be publicly available to allow any physician to input specific patient characteristics to receive results from the models. This in turn will provide information to help facilitate personalized treatment selection and better patient outcomes. The results of this research can also help inform health policy by providing comprehensive information on the factors affecting MS progression and its economic burden.

Funding is provided by the CanProCo study and a project grant from the Canadian Institutes of Health Research (PJT-178251). CanProCo is funded by the Multiple Sclerosis Society of Canada, Brain Canada, Roche, Biogen-Idec, and the Government of Alberta.