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| Name | Class |
|---|---|
| Charite University, Berlin, Germany | OTHER |
| Ruhr University of Bochum | OTHER |
| Technische Universität Dresden | OTHER |
| University Hospital, Lille |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Data from real-world clinical practice | Retrospective, real-world clinical data obtained via the 6 participating clinical centers in the study. | ||
| Data from the control arms of relevant clinical trials | Data from the control arms of relevant clinical trials obtained via the 4 participating pharmaceutical partners in the study. |
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| Measure | Description | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 validity of the data through an assessment of the amount of erroneous or impossible data entries for each variable. |
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Inclusion criteria:
Exclusion criteria:
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The study will include patients with a confirmed diagnosis of MS (Thompson et al., 2018), CIS, RIS, NMOSD (Wingerchuck et al., 2015), or MOGAD (Banwell et al., 2023). No other specifications are included. We envision a database that captures the diversity and heterogeneity of the population, in order to address factors influencing disease worsening that have not been investigated yet or have only been investigated to a very limited extent in previous studies.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Diana M Sima, PhD | Contact | +32 16 369 000 | diana.sima@icometrix.com | |
| Vincenzo Anania | Contact | +32 16 369 000 | vincenzo.anania@icometrix.com |
| Name | Affiliation | Role |
|---|---|---|
| Friedemann Paul, PhD, MD | Max Delbrück Center - Charite University, Berlin, Germany | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| General University Hospital Prague | Recruiting | Prague | Praha 2 | 128 00 | Czechia |
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| Label | URL |
|---|---|
| Website of the EU-funded CLAIMS project of which this study is a part. | View source |
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| OTHER |
| Casa di Cura IGEA | OTHER |
| General University Hospital, Prague | OTHER |
| Hoffmann-La Roche | INDUSTRY |
| Bristol-Myers Squibb | INDUSTRY |
| Imcyse SA | INDUSTRY |
| AB Science | INDUSTRY |
| Nocturne UG | UNKNOWN |
| Aalto University | OTHER |
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| 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 temporal uniformity of the harmonised dataset over time as assessed by the average time between subsequent assessments of each variable. | 4 years |
| 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 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 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. | 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. | 4 years |
| Katholisches Klinikum Bochum - St. Joseph-Hospital | Recruiting | Bochum | Bochum | 44791 | Germany |
|
| ERC Charité - Universitätsmedizin Berlin | Recruiting | Berlin | State of Berlin | 131256 | Germany |
|
| ID | Term |
|---|---|
| D009103 | Multiple Sclerosis |
| D009471 | Neuromyelitis Optica |
| D000098542 | Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disease |
| D018450 | Disease Progression |
| ID | Term |
|---|---|
| D020278 | Demyelinating Autoimmune Diseases, CNS |
| D020274 | Autoimmune Diseases of the Nervous System |
| D009422 | Nervous System Diseases |
| D003711 | Demyelinating Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |
| D009188 | Myelitis, Transverse |
| D009902 | Optic Neuritis |
| D009901 | Optic Nerve Diseases |
| D003389 | Cranial Nerve Diseases |
| D005128 | Eye Diseases |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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