Interoperable Integration of a National Rare Disease Registry Into a Rare Eye Disease Data Warehouse: Implementation...

In France, clinical data on rare diseases are primarily collected through BaMaRa (Base Maladies Rares), a software platform used by national expert centers to populate the BNDMR (Banque Nationale de Données Maladies Rares), the French national rare disease data warehouse. BaMaRa ensures standardized and structured data collection across all rare disease networks, with a focus on care coordination and epidemiological reporting. In 2024, FREDD (French Rare Eye Disease Database), a health data warehouse dedicated to rare eye diseases, was developed within the framework of the third French National Rare Disease Plan by the SENSGENE sector. Despite overlapping datasets, there is no native interoperability between BaMaRa and FREDD, requiring the development of a dedicated, traceable pipeline to transform BaMaRa exports into data suitable for inclusion in FREDD. This transformation involves complex business rules to address structural, semantic, and specific differences between the two systems.

This study aims to describe the design and implementation of a robust data transformation pipeline that enables the automated conversion of BaMaRa clinical records into a structured dataset aligned with the FREDD data model. The primary goal is to ensure that the data remain semantically consistent and reusable for the secondary use of health data.

We developed a Python-based application called FREDDEX that integrates several configuration files and encodes the domain-specific business rules required to align BaMaRa data with the FREDD schema. These rules include patient filtering, mapping of variable names and values, management of multisource redundancy, and prevention of overwriting. The system was designed to be modular, auditable, and usable by clinical data managers with minimal technical expertise.

FREDDEX was tested and validated on a BaMaRa export of 1000 real patients from Strasbourg University Hospital. The tool successfully filtered and created 641 patient profiles in FREDD, with a 99% success rate for attempted imports and full concordance (100%) for directly mapped and inferred variables. Genetic data reconstruction was confirmed on a random sample of 30 patients with genetic information, showing 100% accuracy, and multidiagnostic blocks were correctly handled in all manually reviewed cases. Beyond validation, FREDDEX processed up to 5000 patient records, enabling the rapid onboarding of new clinical sites and significantly reducing manual curation time, while runtime and memory usage demonstrated near-linear scaling. Importantly, the tool established a facilitated reproducible framework adaptable to other rare disease contexts and interoperable with national and European platforms, such as European Reference Network-EYE.

This work demonstrates that transforming structured national rare disease registry data into a research-oriented health data warehouse is feasible when clinical business rules are explicitly formalized within an auditable extract-transform-load framework. Beyond the FREDD use case, this approach illustrates how interoperability between care-based and research infrastructures can be operationalized in rare diseases while preserving semantic integrity and regulatory compliance.