The Dutch Dystrophinopathy Database: A National Registry with Standardized Patient and Clinician Reported Real-World Data

Abstract

Background: Duchenne and Becker muscular dystrophy lack curative treatments. Registers can facilitate therapy development, serving as a platform to study epidemiology, assess clinical trial feasibility, identify eligible candidates, collect real-world data, perform post-market surveillance, and collaborate in (inter)national data-driven initiatives. Objective: In addressing these facets, it’s crucial to gather high-quality, interchangeable, and reusable data from a representative population. We introduce the Dutch Dystrophinopathy Database (DDD), a national registry for patients with DMD or BMD, and females with pathogenic DMD variants, outlining its design, governance, and use. Methods: The design of DDD is based on a system-independent information model that ensures interoperable and reusable data adhering to international standards. To maximize enrollment, patients can provide consent online and participation is allowed on different levels with contact details and clinical diagnosis as minimal requirement. Participants can opt-in for yearly online questionnaires on disease milestones and medication and to have clinical data stored from visits to one of the national reference centers. Governance involves a general board, advisory board and database management. Results: On November 1, 2023, 742 participants were enrolled. Self-reported data were provided by 291 Duchenne, 122 Becker and 38 female participants. 96% of the participants visiting reference centers consented to store clinical data. Eligible patients were informed about clinical studies through DDD, and multiple data requests have been approved to use coded clinical data for quality control, epidemiology and natural history studies. Conclusion: The Dutch Dystrophinopathy Database captures long-term patient and high-quality standardized clinician reported healthcare data, supporting trial readiness, post-marketing surveillance, and effective data use using a multicenter design that is scalable to other neuromuscular disorders.