Designing an integrated data model for prospective genotype-phenotype in inborn errors of immunity research.

Background: Inborn errors of immunity are rare, genetically heterogeneous disorders requiring coordinated clinical, laboratory, and genetic evaluation over time. Data are often fragmented across records, laboratory systems, and genomic reports, limiting longitudinal analysis and coordinated care, particularly in the Middle East and North Africa, where structured rare disease data infrastructures remain limited. Objective: To develop a Research Electronic Data Capture-based data management framework for inborn errors of immunity and demonstrate its use in a prospective multi-site setting. Methods: A Research Electronic Data Capture-based framework was developed at the College of Medicine and Health Sciences, United Arab Emirates University. Modular instruments captured consent, demographics, biospecimen processing, laboratory workflows, and genetic findings within a longitudinal structure. Data dictionaries, validation rules, and conditional logic ensured data quality. The framework was deployed across participating sites for prospective data collection. Results: The framework enabled integrated longitudinal documentation of enrollment, biospecimens, and genetic testing. It was implemented across two clinical sites and used to enroll patients with suspected or confirmed inborn errors of immunity. The platform supported standardized cross-site data capture and monitoring of genetic findings, including automated flagging of variants of uncertain significance. Conclusion: This study demonstrates the development and early multi-site implementation of a Research Electronic Data Capture-based framework for inborn errors of immunity. By enabling standardized integration of clinical, laboratory, and genetic data, the platform supports data quality, cross-site collaboration, and tracking of evolving diagnoses. It provides a scalable foundation for rare disease research and may support improved clinical decision-making.