Background
Multiple sclerosis (MS) is the most common immunological disorder of the nervous system (> 1 in 1000). This disease has a strong female preponderance and is the most common cause of permanent disability in young adults. MS affects the central nervous system causing a variety of neurological symptoms escalating disability over time. Despite the availability of a large panel of approved disease-modifying MS drugs, frequently physicians lack data-based support to prescribe the most effective treatment at a given time point along the patient pathway, highlighting the need for personalized clinical management. Artificial intelligence (AI)-driven solutions hold a high potential for precision medicine but have not yet fulfilled expectations. Thanks to a trans-regional collaboration between institutions from four countries (Switzerland, Germany, France, and, Luxembourg), CLINNOVA has the mission to support the digitalization of healthcare, unlock healthcare’s AI potential, and improve personalized treatments of chronic inflammatory diseases. The CLINNOVA consortium focuses on 3 Use Cases of immune-related diseases with large unmet medical burden: multiple sclerosis, inflammatory bowel diseases and, rheumatoid diseases.
Research Question
CLINNOVA-MS aims to build a prospective MS patient cohort across the participating clinical centers to generate structured, standardized, interoperable, quality-controlled, multi-sourced, and multi-dimensional data. These data will be analyzed in a federated learning platform to train and develop AI-algorithms generating personalized therapy solutions for patients, identify characteristics associated with early MS and transitioning phases to progressive MS, and validate digital biomarkers allowing a continuous monitoring of those patients.
Study Methodology
CLINNOVA-MS is a prospective cohort study including participants with early MS or in the transitioning phase to progressive MS. Up to 100 participants will be enrolled in the study in Basel and an equivalent number should be also enrolled at each participating center.Eligible patients will be asked to provide data and samples and will be followed up for 5 years starting from the date of inclusion. Patients reported outcomes and cognitive and motor assessments will be performed using the Healios+Me smartphone application. Biological samples (i.e., blood, cerebrospinal fluid, and stool) as well as imaging data (if performed as per standard of care) will be collected locally, then processed and analyzed centrally at the appropriate partner institutions. The sample-associated data, monitoring data collected via the digital application and patient visits data will be analyzed through a privacy-preserving platform using federated learning. This approach allows to train algorithms on datasets from different clinical sites without sharing sensitive data.
Significance of the study
Thanks to its consortium, CLINNOVA-MS will make a significant step forward to facilitate early MS detection, enable personalized medicine approaches through AI‐based stratification, and improve patients’ care and quality of life while reducing healthcare costs.
Contributors
Prof. Ludwig Kappos (RC2NB, USB)
PD Dr. Lars G. Hemkens, MPH (RC2NB, USB)
Dr. Bebeka Cosandey (RC2NB, USB)
Dr. Amandine Bovay (University of Basel)
Dr. Bram Stieltjes (University Hospital Basel)
Dr. Thierry Sengstag (University of Basel)
