Grant: Project Grant

Multiple sclerosis (MS) is a progressive disease of the brain and spinal cord, affecting over 2.3 million people worldwide and 93,500 people in Canada. Canada has one of the highest rates of MS globally and it is estimated that the number of people living with MS in Canada will increase to approximately 33,600 in 2031. MS mainly affects people between 15 and 40 years of age, placing a significant strain not only on patients in the prime of their life, but also on the healthcare system. Although there is no cure for MS, there are treatments available to help manage episodes of increased disability (relapses), reduce relapses, and control symptoms. Treatment with drugs known as interferon-betas were the first medications for MS and being the only promising therapeutic option initially for many years, treatment selection was limited. An increasing number of new treatments have emerged since then, however, there are limited evidence based guidelines concerning the use and selection of treatments for the various disease states of MS. Additionally, while there have been many studies that have identified specific independent risk factors for patient health outcomes, no study to date has attempted to combine these patient, disease, and treatment-related factors into a complete model to enhance the prediction of how patients will respond to treatment and whether or not they will improve. The objective of this study is to use patient data from provincial healthcare records as well as clinical data collected over an extended time period to develop and test of a series of predictive models in MS, and make these models freely available on a web-based platform. Ultimately, these models will help guide the selection of the best therapeutic approach for each individual, which will improve decision-making at the patient level. In addition, these predictions should result in better patient care as well as a more efficient and cost-effective use of healthcare resources.