nctId: NCT06280755 id: ICO-S-002 briefTitle: Clinical Impact Through AI-assisted MS Care - A Retrospective Multi-center Observational Study. acronym: RECLAIM overallStatus: NOT_YET_RECRUITING date: 2024-03 date: 2027-04 date: 2027-04 date: 2024-02-28 date: 2024-02-28 name: icometrix class: INDUSTRY name: Charite University, Berlin, Germany name: Ruhr University of Bochum name: Technische Universität Dresden name: University Hospital, Lille name: Casa di Cura IGEA name: General University Hospital, Prague name: Hoffmann-La Roche name: Bristol-Myers Squibb name: Imcyse SA name: AB Science name: Nocturne UG name: Aalto University briefSummary: 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. conditions: Multiple Sclerosis conditions: NMO Spectrum Disorder conditions: Myelin Oligodendrocyte Glycoprotein Antibody-associated Disease conditions: Radiologically Isolated Syndrome conditions: Clinically Isolated Syndrome studyType: OBSERVATIONAL observationalModel: OTHER timePerspective: RETROSPECTIVE count: 7000 type: ESTIMATED measure: The number of patients from each institution who have contributed data to the database. measure: The number of patients from each institution whose data was mapped to the common data model of the harmonised database. measure: The number of patients from the control arms of clinical trials who have contributed data to the database. measure: The data completeness of each variable in the harmonised database. measure: 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 measure: The validity of the data through an assessment of the amount of erroneous or impossible data entries for each variable. measure: 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). measure: The temporal uniformity of the harmonised dataset over time as assessed by the average time between subsequent assessments of each variable. measure: The presence of contextual information on standard data gathering and analysis processes of each institution measure: The presence of a unique and pseudonymised patient ID for all data of each patient, allowing to link such data of each patient. measure: 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. measure: The percentage of MRI data sets which are compliant with the MAGNIMS-CMSC-NAIMS acquisition guidelines. measure: The percentage of MRI data sets for which the automated quality control process of icobrain ms did not indicate any quality issues upon analysis. measure: The percentage of patients with a complete disease modifying treatment history available, from the date of diagnosis to the current day. measure: The percentage of patients with a complete disease history available, from the date of diagnosis to the current day. measure: The validity and temporal uniformity for disability assessment as clinically determined by EDSS, Functional systems score, T25FWT, 9HPT and SDMT. sex: ALL minimumAge: 18 Years stdAges: ADULT stdAges: OLDER_ADULT hasResults: False