25gmds190 10.3205/25gmds190 urn:nbn:de:0183-25gmds1904 Meeting Abstract Pérez Garriga Pérez Garriga Ariadna A

Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany

author Honrath Honrath Philipp P

Department of Neurology, Epilepsy Section, RWTH Aachen University, Aachen, Germany

author Wolking Wolking Stefan S

Uniklinikum RWTH Aachen, Aachen, Germany

author Coldewey Coldewey Beatrice B

Medical Faculty, RWTH Aachen University, Aachen, Germany

author Bozkir Bozkir Susann S

Uniklinikum RWTH Aachen, Aachen, Germany

author Freyer Freyer Nils N

Uniklinikum RWTH Aachen, Aachen, Germany

author May May Patrick P

University of Luxembourg, Esch-sur-Alzette, Luxembourg

author Weber Weber Yvonne Y

Department of Neurology, Epilepsy Section, RWTH Aachen University, Aachen, Germany

author Röhrig Röhrig Rainer R

Medizinische Fakultät der RWTH Aachen, Aachen, Germany

author Lipprandt Lipprandt Myriam M

Institute of Medical Informatics, University Hospital RWTH Aachen, Aachen, Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 epilepsy genetics data standardization and transformation Common Data Model ETL 20260401 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 190 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) V: Bioinformatics, Omics data and precision medicine Jena 20250907 20250911 Abstr. 24 TextIntroduction: Understanding disease development increasingly relies on integrating diverse data sources, such as care records, cohort data, and genetic information. This integration reveals complex links between disease onset and progression, making the digital representation of multimodal data essential. Genomic data plays a pivotal role in precision medicine , , especially for complex conditions like epilepsy . Advances in gene discovery have significantly improved rare disease diagnostics. However, the growing number of known disease-associated genes also complicates interpretation. Standardized data models and collaboration are key to effectively using genomic information in research and care. In oncology, molecular tumor boards help tailor treatments through interdisciplinary collaboration. A similar approach is needed in neurology: molecular epilepsy boards can guide precision therapies based on the latest genetic insights.As part of the BMBF-funded TreatION project, we developed a demonstrator for such a board.Methods: Our approach begins with a requirements analysis to guide the integration of diverse, health-related data for precision medicine. This includes patient data, diagnostic measurements (e.g., MRI, EEG), and external sources. The goal is to design a flexible, scalable data model that allows for interchangeable outputs and avoids tool-specific dependencies.Centralizing this multimodal data on secure hospital servers is key. Genetic variant information from research literature must also be stored for use in pathogenicity scoring. This structured integration supports advanced analytics, clinical decision-making, and research.Relational databases, though scalable, are limited by rigid schemas. Alternatives like NoSQL or RDF offer flexibility but pose interoperability and usability challenges. A hybrid approach using relational databases in an Entity-Attribute-Value (EAV) format provides the needed flexibility, but it must be supported by robust metadata to ensure data usability.TreatION hospitals currently collect patient data using REDCap forms, which vary across sites. A key task is transforming these into a unified, adaptable format that supports future expansion. Results: To enable rapid access to existing and future data, we implemented a hybrid EAV database model, avoiding table changes and allowing flexible schema extension. The database includes three main tables: Patient: Stores immutable demographic data (e.g., birthdate, sex, ethnicity), with data origin tracking.Clinical Findings: Uses an EAV format to record variable data (e.g., diagnoses, MRI, EEG, and genetic results). Each entry includes a parameter, value, coding system, and metadata (e.g., source, contributor). REDCap forms are mapped into this structure, with grouped fields linked by a cluster ID.Literature: Compiles curated knowledge from OMIM, ClinVar, and gnomAD in EAV format for reference during variant interpretation.For visualization and exploration, we integrated the data model with cBioPortal, enabling dashboard-driven analysis of clinical, mutation, and literature data relevant to epilepsy.????Conclusion: We developed a hybrid EAV-based data model for integrating heterogeneous clinical and genomic data in precision epilepsy medicine, demonstrated in the TreatION project. The model supports flexibility to incorporate new datasets, thereby seeding the effective use of the data, standardized mapping, and scalability, with future compatibility toward frameworks like OMOP CDM. Integration with cBioPortal enables effective visualization and analysis, laying the groundwork for interoperable and collaborative precision medicine tools.The authors declare that they have no competing interests.The authors declare that an ethics committee vote is not required. Tesi B Boileau C Boycott KM Canaud G Caulfield M Choukair D Precision medicine in rare diseases: What is next? 2023 J Intern Med 397–412 Tesi B, Boileau C, Boycott KM, Canaud G, Caulfield M, Choukair D, et al. Precision medicine in rare diseases: What is next? J Intern Med. 2023;294:397–412. DOI: 10.1111/joim.13655 http://dx.doi.org/10.1111/joim.13655 Knowles JK Helbig I Metcalf CS Lubbers LS Isom LL Demarest S Precision medicine for genetic epilepsy on the horizon: Recent advances, present challenges, and suggestions for continued progress 2022 Epilepsia 2461–75 Knowles JK, Helbig I, Metcalf CS, Lubbers LS, Isom LL, Demarest S, et al. 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Oncologist. 2014;19:631–6. DOI: 10.1634/theoncologist.2013-0405 http://dx.doi.org/10.1634/theoncologist.2013-0405 Khan O Lim Choi Keung SN Zhao L Arvanitis TN A Hybrid EAV-Relational Model for Consistent and Scalable Capture of Clinical Research Data 2014 Integrating Information Technology and Management for Quality of Care 32–5 Khan O, Lim Choi Keung SN, Zhao L, Arvanitis TN. A Hybrid EAV-Relational Model for Consistent and Scalable Capture of Clinical Research Data. Integrating Information Technology and Management for Quality of Care. 2014:32–5. DOI: 10.3233/978-1-61499-423-7-32 http://dx.doi.org/10.3233/978-1-61499-423-7-32 0 0 0 0