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

Area of Science:

• Immunology
• Genetics
• Data Science

Background:

• Inborn errors of immunity (IEI) are rare, genetically diverse conditions needing coordinated care.
• Fragmented data across systems hinders longitudinal analysis and care, especially in regions with limited rare disease infrastructure.
• Effective data management is crucial for advancing the understanding and treatment of IEI.

Purpose of the Study:

• To develop a Research Electronic Data Capture (REDCap)-based data management framework tailored for IEI.
• To demonstrate the framework's utility in a prospective, multi-site research setting.

Main Methods:

• A modular REDCap framework was created, capturing consent, demographics, biospecimens, lab workflows, and genetic findings longitudinally.
• Data quality was ensured through data dictionaries, validation rules, and conditional logic.
• The framework was deployed across multiple clinical sites for prospective data collection.

Main Results:

• The framework facilitated integrated, longitudinal documentation of patient enrollment, biospecimens, and genetic testing.
• It was successfully implemented across two clinical sites for patients with suspected or confirmed IEI.
• The platform enabled standardized data capture, cross-site monitoring, and automated flagging of genetic variants.

Conclusions:

• The study successfully developed and implemented a REDCap-based framework for IEI data management.
• This standardized integration of clinical, laboratory, and genetic data enhances data quality, collaboration, and diagnosis tracking.
• The framework offers a scalable solution for rare disease research and can support improved clinical decision-making.