Clinical trial NCT06280755: Clinical Impact Through AI-assisted MS Care - A Retrospective Multi-center Observational Study.
Official title: Clinical Impact Through AI-assisted MS Care - A Retrospective Multi-center Observational Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Multiple Sclerosis
NMO Spectrum Disorder
Myelin Oligodendrocyte Glycoprotein Antibody-associated Disease
Radiologically Isolated Syndrome
Clinically Isolated Syndrome
Keywords: Multiple Sclerosis
Prognosis
Progression
AI models
disease worsening
Lead sponsor: icometrix
Sponsor class: INDUSTRY
Collaborators: 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
First posted: 2024-02-28
Enrollment: 7000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: 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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: The number of patients from each institution who have contributed data to the database.
timeFrame: 4 years
measure: The number of patients from each institution whose data was mapped to the common data model of the harmonised database.
timeFrame: 4 years
measure: The number of patients from the control arms of clinical trials who have contributed data to the database.
timeFrame: 4 years
measure: The data completeness of each variable in the harmonised database.
timeFrame: 4 years
Secondary outcomes:
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
timeFrame: 4 years
measure: The validity of the data through an assessment of the amount of erroneous or impossible data entries for each variable.
timeFrame: 4 years
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).
timeFrame: 4 years
measure: The temporal uniformity of the harmonised dataset over time as assessed by the average time between subsequent assessments of each variable.
timeFrame: 4 years
measure: The presence of contextual information on standard data gathering and analysis processes of each institution
timeFrame: 4 years
measure: The presence of a unique and pseudonymised patient ID for all data of each patient, allowing to link such data of each patient.
timeFrame: 4 years
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.
timeFrame: 4 years
measure: The percentage of MRI data sets which are compliant with the MAGNIMS-CMSC-NAIMS acquisition guidelines.
timeFrame: 4 years
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.
timeFrame: 4 years
measure: The percentage of patients with a complete disease modifying treatment history available, from the date of diagnosis to the current day.
timeFrame: 4 years
measure: The percentage of patients with a complete disease history available, from the date of diagnosis to the current day.
timeFrame: 4 years
measure: The validity and temporal uniformity for disability assessment as clinically determined by EDSS, Functional systems score, T25FWT, 9HPT and SDMT.
timeFrame: 4 years
Other outcomes:
Locations:
city: Prague
country: Czechia
facility: General University Hospital Prague
state: Praha 2
city: Berlin
country: Germany
facility: ERC Charité - Universitätsmedizin Berlin
city: Bochum
country: Germany
facility: Katholisches Klinikum Bochum - St. Joseph-Hospital
Study officials:
affiliation: Max Delbrück Center - Charite University, Berlin, Germany
name: Friedemann Paul, PhD, MD
role: PRINCIPAL_INVESTIGATOR
References: