Studying rare immunodeficiency conditions or inborn errors of immunity (IEI) on a large scale is important to understand their natural history, associated morbidities, and long-term outcomes. Compiling such comprehensive data is necessary to gain a detailed understanding of these conditions and improve treatment and care for those affected. To support researchers in this effort, we created United States Immunodeficiency Network (USIDNET) registry V2, a secure, structured, standardized database with automated yearly updates for enrolled patients across the United States. This design offers access to current and consistent information. Standardized data fields ensure harmonization across sites, and the availability of longitudinal data will afford new insights into disease evolution and therapeutic responses.
USIDNET utilizes our Translational Data Warehouse (TDW) system, built with PostgreSQL, to store and manage data securely. To streamline this effort and minimize the burden on participating sites, we created an automated data extraction tool consisting of an extract, transform, and load (ETL) process to combine all the data. This process collects data from multiple sites that execute USIDNET registry V2 queries and stores the data in a consistent format. We implemented a dual de-identification method: receive all data in a de-identified format from participating sites (passive de-identification) and then perform a second layer of de-identification on all IDs (active de-identification) by assigning a new unique ID to each record. These newly assigned IDs are automatically included in subsequent tables. This technique ensures that there are no duplicate IDs in the combined dataset.
Since August 2024, we have successfully enrolled 1,145 patients into the registry, with multiple additional sites initiating their data extraction processes. The ages range from 1 to 58 years and are roughly equal in sexes. USIDNET represents a community-driven approach to understanding the evolution of disease, comorbidities, and response to intervention. Semiautomated data extraction enables a structured collection of longitudinal data, which is important for IEI because the phenotypes evolve over time. De-identified data collection with a waiver of consent enables more complete ascertainment. The current USIDNET registry V2 is a growing endeavor to support longitudinal research, while the original USIDNET registry V1 continues to support cross-sectional research on IEI.
