Clinically Isolated Syndrome, Multiple Sclerosis, Myelin Oligodendrocyte Glycoprotein Antibody-associated Disease, NMO Spectrum Disorder, Radiologically Isolated Syndrome
Conditions
Keywords
Multiple Sclerosis, Prognosis, Progression, AI models, disease worsening
Brief summary
The RECLAIM study aims to gather a centralized and harmonized dataset, enabling the secondary use of data for building AI-based models that will support diagnosis and prognosis of individual Multiple Sclerosis patient's disease course and treatment response in a real-world setting. Additionally, the data will be used to generate further insights on Multiple Sclerosis progression as well as to develop the tools to monitor this progression.
Detailed description
There is a clear need for a data-driven and personalized treatment optimisation tool for people with Multiple Sclerosis (MS), in order to enable/support physicians to deploy appropriate therapeutic measures that will help to better slow down disease progression and eventually, progressive disability worsening. While early diagnosis and prognostic modelling is important to make data-driven recommendations for treatment optimisation, being able to disentangle and monitor the disability accumulation due to 'relapse associated worsening' or due to 'progression independent of relapse activity' will be key to optimizing treatment for the best possible long-term outcomes. The latter strongly depends on the availability of biomarkers that can detect and differentiate between these different forms of disease worsening. With the RECLAIM study, we focus on gathering a centralized and harmonized dataset, enabling the secondary use of data to support prognosis for people with MS, as well as treatment optimisation in a real-world setting. As such, RECLAIM aims to develop MRI-based tools to better monitor disease progression in people with MS, as well as AI-based models that will support prognosis of individual disease course and treatment response, comprising: (i) a biomarker-based MS progression model, (ii) an MRI-focused generative model to predict brain characteristic evolution, and (iii) an interventional model for treatment optimisation. Additionally, the data will be used to generate further insights on Multiple Sclerosis progression as well as to develop the tools to monitor this progression.
Interventions
None listed
Sponsors
Charite University, Berlin, Germany
Ruhr University of Bochum
Technische Universität Dresden
University Hospital, Lille
Casa di Cura IGEA
General University Hospital, Prague
Hoffmann-La Roche
Bristol-Myers Squibb
Imcyse SA
AB Science
Nocturne UG
Aalto University
icometrix
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Patients must have a confirmed diagnosis of MS, NMOSD, MOGAD, CIS or RIS. * Patient (or patient's legal representative) has previously signed and dated an informed consent form (ICF) for the secondary use of their data, or assent form. Alternatively, the secondary use of the patient's data is allowed following Institutional Review Board (IRB)/Ethical Committee (EC) approval in accordance with national and local subject privacy regulations.
Exclusion criteria
* Patients under 18 years of age will be excluded. * Other unspecified reasons that, in the opinion of the Investigator or Joint Steering Committee, make the patient unsuitable for participation in the study.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| The number of patients from each institution who have contributed data to the database. | 4 years |
| The number of patients from each institution whose data was mapped to the common data model of the harmonised database. | 4 years |
| The number of patients from the control arms of clinical trials who have contributed data to the database. | 4 years |
| The data completeness of each variable in the harmonised database. | 4 years |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The presence of contextual information on standard data gathering and analysis processes of each institution | 4 years | — |
| The presence of a unique and pseudonymised patient ID for all data of each patient, allowing to link such data of each patient. | 4 years | — |
| The temporal uniformity of MRI data over time as assessed by the comparability of MRI scans and the average time between subsequent MRI assessments for each patient. | 4 years | — |
| The percentage of MRI data sets which are compliant with the MAGNIMS-CMSC-NAIMS acquisition guidelines. | 4 years | — |
| The validity of the data through an assessment of the amount of erroneous or impossible data entries for each variable. | 4 years | — |
| The percentage of patients with a complete disease modifying treatment history available, from the date of diagnosis to the current day. | 4 years | — |
| The percentage of patients with a complete disease history available, from the date of diagnosis to the current day. | 4 years | — |
| The validity and temporal uniformity for disability assessment as clinically determined by EDSS, Functional systems score, T25FWT, 9HPT and SDMT. | 4 years | Each of these scores will be assessed individually for the amount of erroneous or impossible data entries, as well as for the average time between subsequent assessments of each variable. |
| The percentage of MRI data sets for which the automated quality control process of icobrain ms did not indicate any quality issues upon analysis. | 4 years | — |
| The temporal uniformity of each institution's data over time as assessed by the number of changes to variables over time (addition of new variables or variables no longer being captured, alterations to how variables are captured). | 4 years | — |
| The representativeness of the harmonised dataset for the MS patient population as evaluated by age range, gender balance, the distribution of country of residence, the distribution of race/ethnicity and the distribution of educational level | 4 years | — |
| The temporal uniformity of the harmonised dataset over time as assessed by the average time between subsequent assessments of each variable. | 4 years | — |
Countries
Czechia, Germany
Contacts
Primary ContactDiana M Sima, PhD
Backup ContactVincenzo Anania
Outcome results
None listed