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The Austrian MS database and the Austrian MS cohort : A national effort towards data harmonization and prospective
Gabriel Bsteh1,2, Fabian Föttinger3,4, Markus Ponleitner3,4
1Department of Neurology, Medical University of Vienna, Vienna, Austria. gabriel.bsteh@meduniwien.ac.at.
Background:
Multiple sclerosis (MS) is treated with various disease-modifying therapies (DMTs) with differing efficacy and risks, yet the disease course varies markedly within and between individuals. Defining optimal treatment strategies through randomized trials is impractical because of the required sample sizes, costs and long follow-up. Large multicenter registries and prospective observational cohorts are therefore essential but demand harmonized, standardized, user-friendly data capture with rigorous quality control and data protection.
Objective:
This project aims to establish standardized, nationwide MS data collection in Austria.
Methods:
The project consists of five key components: (i) harmonization of data collection, (ii) creation of infrastructure for data sharing, (iii) retrospective harmonized data collection (Austrian MS Database, AMSD), (iv) prospective harmonized data collection (Austrian MS Cohort, AMSC) and (v) aggregated analyses.
Results:
A comprehensive set of harmonized common data elements (CDE) comprising clinical and paraclinical data was developed and a common data collection infrastructure was generated using the web-based Research, Documentation, and Analysis platform (webRDA), an innovative data capture, processing and analysis system provided by the Medical University of Vienna offering pseudonymized storage of data supported by a robust permissions system fulfilling legal data protection and ethical requirements. The AMSC is set up as a standardized prospective collection of demographic, clinical, epidemiological, psychosocioeconomic, magnetic resonance imaging (MRI), and optical coherence tomography (OCT) data as well as body fluids.
Conclusion:
The AMSD and AMSC will facilitate the evidence-based development of prognostic biomarkers, individualized therapy strategies and treatment sequences based on a high-quality, population-based dataset of more than 8000 people with MS.
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