Amy M. FitzPatrick ¹ Aaron T. Chin ² Sharon Nirenberg ³ Charlotte Cunningham-Rundles ⁴ Keith Sacco ⁵ Jesse Perlmutter ⁶ Joseph Francis Dasso ⁷ Athanasios Tsalatsanis ⁸ Jay Sharad Maru ⁸ Jessica Creech ⁷ Jolan Eszter Walter ⁷ Nicholas Hartog ^10,9 Neema Izadi ¹¹ Mandy Palmucci ¹² Manish Butte ¹³ Klaus Loewy-Valencia ¹⁴ Anurag Relan ¹⁵ Nicholas L Rider ^16*

• ¹ PRECISIONAQ, Bethesda, United States
• ² Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California, United States
• ³ Division of Informatics and Data Architecture, Icahn School of Medicine, Departments of Scientific Computing and Data, Mount Sinai School of Medicine,, New York City, United States
• ⁴ Division of Clinical Immunology, Icahn School of Medicine, Departments of Medicine and Pediatrics, Mount Sinai School of Medicine, New York City, United States
• ⁵ Department of Child Health, University of Arizona College of Medicine and Division of Pulmonology, Section of Allergy-Immunology, Phoenix Children's Hospital, Phoenix, United States
• ⁶ Integration & Data, Phoenix Children's Hospital, Phoenix, United States
• ⁷ Department of Pediatric Allergy and Immunology, University of South Florida at Johns Hopkins All Children's Hospital, St. Petersburg, United States
• ⁸ Research Methodology and Biostatistics Core, Morsani College of Medicine, University of South Florida Health, St. Petersburg, United States
• ⁹ Division of Allergy and Immunology, Helen DeVos Children's Hospital and Corewell Health, Grand Rapids, United States
• ¹⁰ College of Human Medicine, Michigan State University, East Lansing, Michigan, United States
• ¹¹ Division of Clinical Immunology and Allergy, Children's Hospital Los Angeles, Los Angeles, United States
• ¹² Division of Information Services, Children's Hospital Los Angeles, University of Southern California, Los Angeles, United States
• ¹³ Department of Pediatrics and Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, United States
• ¹⁴ Department of Information Services, Texas Children’s Hospital, Houston, United States
• ¹⁵ Pharming Healthcare, Inc., Warren, United States
• ¹⁶ Department of Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Division of Allergy-Immunology Carilion Clinic, Roanoke, United States

Introduc)on: PaDents with acDvated PI3Kδ syndrome (APDS) may elude diagnoses for nearly a decade. Methods to hasten the idenDficaDon of these paDents, and other paDents with inborn errors of immunity (IEIs), are needed. We sought to demonstrate that querying electronic health record (EHR) systems by aggregaDng disparate signs into a risk score can idenDfy these paDents. Methods: We developed a structured query language (SQL) script using literature-validated APDS-associated clinical concepts mapped to ICD-10-CM codes. We ran the query across EHRs from 7 large, US-based medical centers encompassing approximately 17 million paDents. The query calculated an "APDS Score," which straDfied risk for APDS for all individuals in these systems. Scores for all known paDents with APDS (n=46) were compared. Results: The query idenDfied all but one known paDent with APDS (98%; 45/46) as well as paDents with other complex disease. Median score for all paDents with APDS was 9 (IQR = 5.75; range 1-25). SensiDvity analysis suggested an opDmal cutoff score of 7 (sensiDvity = 0.70). Conclusion: Diseasespecific queries are a relaDvely simple method to foster paDent idenDficaDon across the rare-disease spectrum. Such methods are even more important for disorders such as APDS where an approved, pathway-specific treatment is available in the US.