Automated data extraction model for the USIDNET registry: Bigger, faster, and better data collection
Year: 2025
Type: article
Study design: Observational Study
Abstract: The United States Immunodeficiency Network (USIDNET) is an NIH-funded research consortium that advances scientific investigation on inborn errors of immunity (IEIs). Formerly, USIDNET registry data were collected via an opt-in system with patient informed consent. The current registry uses semiautomated, de-identified data extraction from EPIC with a consent waiver. This study was performed to assess whether the new method improved enrollment using data from one site. Diagnoses, sex, and age within the new (n = 2,796) and old (n = 551) registries were defined. The new registry enrolled more subjects and had six times more clinical features recorded per patient on average and 22 times more laboratory data recorded per patient. Response to queries is much more rapid, with execution of a database query within a day. There were differences in enrollment demographics depending on underlying diagnosis. The design of the new USIDNET registry may better capture a greater number and representation of patients compared to the old registry.
Source: Journal of Human Immunity
Authors: Vaibhavi Vichare, Gonench Kilich, Charlotte Cunningham-Rundles, Michelle Onyekaba, Roshini S. Abraham, Jennifer Puck, Ramsay Fuleihan, Rebecca A. Marsh, Kathleen E. Sullivan
Institutions: Children's Hospital of Philadelphia, Icahn School of Medicine at Mount Sinai, University of Pennsylvania, Nationwide Children's Hospital, University of California, San Francisco, UCSF Benioff Children's Hospital, Columbia University Irving Medical Center, Cincinnati Children's Hospital Medical Center, University of Cincinnati
Language: English
Subfield: Immunology
Field: Immunology and Microbiology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W7106157368