Their work, published in Nature Medicine, could be a key-stepping stone toward unifying the understanding of this disease by providing a data-driven examination of MS disease evolution. The results may also have practical implications for the management of MS and for the discovery of new therapies.
MS affects around 2.9 million people around the world. There is no cure for the condition; available treatments can reduce the risk of neurological symptoms and hinder the disease from worsening. However, over time, most patients progress and accumulate disability. The treatment of progression remains an urgent unmet medical need.
Disease classification impacts people living with MS, as it defines who is eligible for available medications. It also influences the discovery of new therapies, as it defines the populations that must be studied in clinical trials. However, the traditional way that MS has been divided into distinct subgroups has not accurately reflected the underlying biology of the disease. Increasingly complex and divergent views and sub-classifications have been proposed but there remains a need for a unified approach to classification that meaningfully reflects disease pathology.
In their study, the multidisciplinary team developed a bespoke AI model and interpreted the data from over 8,000 people with MS who were followed for up to 15 years to examine disease evolution over time. The work aimed to either confirm the traditional subtyping of MS, or to propose a new data-driven classification based on the pattern of progression observed in these patients. The results were validated in independent clinical trial and real-world data from another 4,000 people with MS.
Read the full story on the Nuffield Department of Medicine website.