In December 2023, the Trish MS Research Foundation was delighted to announce the recipient of the Trish Multiple Sclerosis Research Foundation Fellowship, with immense gratitude to the Foundation’s very valued Sponsors, donors and supporters for their exceptionally generous contribution to this Fellowship.
Following the Foundation’s honorary Scientific Research Committee meeting, the 5-year $500,000 Trish Multiple Sclerosis Research Foundation Fellowship, commencing January 2024, was awarded to Dr Chao Zhu, Monash University. Dr Zhu is working with Associate Investigators Professor Helmut Butzkueven and A/Prof Anneke van der Walt.
In the first year of his Fellowship, Dr Zhu has made impressive progress.
Dr Zhu’s project aims to support people living with multiple sclerosis (MS) who are considering reducing or stopping strong disease-modifying therapies (DMTs) due to long-term safety concerns. While these treatments are effective, they may also increase the risk of infections or other complications over time. There is currently no tool to help patients and doctors estimate the risks of stepping down treatment in a personalised way.
Over the past year, Dr Zhu developed the first version of a clinical risk scoring system, called the DR-MS score, which helps predict the chance of experiencing serious health events, such as worsening disability, increased relapses, or severe infections, within three years after stopping or switching from a strong MS treatment. Dr Zhu used data from over 1,200 patients from the MSBase international registry and built a scoring system based on common clinical features like age, disability level, relapse history, and treatment background. The tool can be used either through a mathematical formula or a simple point-based table that clinicians can easily apply in practice.
The next step is to test this risk score in independent patient groups from other countries to ensure it works reliably in different settings.
Dr Zhu will use external datasets from the Big MS Data (BMSD) network, including registries from Denmark and France. These datasets offer the opportunity to assess the model’s performance across geographically and clinically diverse populations, thus testing its generalisability and robustness beyond the MSBase dataset used for model development.