Piloting an automated query and scoring system to facilitate APDS patient identification from health systems

Amy M FitzPatrick; Aaron T Chin; Sharon Nirenberg; Charlotte Cunningham-Rundles; Keith Sacco; Jesse Perlmutter; Joseph F Dasso; Athanasios Tsalatsanis; Jay Maru; Jessica Creech; Jolan E Walter; Nicholas Hartog; Neema Izadi; Mandy Palmucci; Manish J Butte; Klaus Loewy; Anurag Relan; Nicholas L Rider

doi:10.3389/fimmu.2024.1508780

Piloting an automated query and scoring system to facilitate APDS patient identification from health systems

Front Immunol. 2025 Jan 21:15:1508780. doi: 10.3389/fimmu.2024.1508780. eCollection 2024.

Authors

Amy M FitzPatrick¹, Aaron T Chin², Sharon Nirenberg³, Charlotte Cunningham-Rundles⁴, Keith Sacco⁵, Jesse Perlmutter⁶, Joseph F Dasso⁷, Athanasios Tsalatsanis⁸, Jay Maru⁹, Jessica Creech¹⁰, Jolan E Walter⁷, Nicholas Hartog¹¹, Neema Izadi¹², Mandy Palmucci¹³, Manish J Butte¹⁴, Klaus Loewy¹⁵, Anurag Relan¹⁶, Nicholas L Rider¹⁷

Affiliations

¹ Precision AQ, Bethesda, MD, United States.
² Department of Pediatrics, University of California, Los Angeles, Los Angeles, CA, 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, NY, United States.
⁴ Division of Clinical Immunology, Icahn School of Medicine, Departments of Medicine and Pediatrics, Mount Sinai School of Medicine, New York, NY, 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, AZ, United States.
⁶ Phoenix Children's Hospital, Phoenix, AZ, United States.
⁷ Department of Pediatric Allergy and Immunology, University of South Florida at Johns Hopkins All Children's Hospital, St. Petersburg, FL, United States.
⁸ Research Methodology and Biostatistics Core, Morsani College of Medicine, University of South Florida Health, St. Petersburg, FL, United States.
⁹ Management Analyst, Research Methodology and Biostatistics Core, Morsani College of Medicine, University of South Florida Health, St. Petersburg, FL, United States.
¹⁰ Department of Pediatrics, University of South Florida at Johns Hopkins All Children's Hospital, St. Petersburg, FL, United States.
¹¹ Division of Allergy and Immunology, Helen DeVos Children's Hospital and Corewell Health, Grand Rapids, Michigan State University College of Human Medicine, East Lansing, MI, United States.
¹² Division of Clinical Immunology and Allergy, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, United States.
¹³ Division of Information Services, Children's Hospital Los Angeles, University of Southern California, Los Angeles, CA, United States.
¹⁴ Department of Pediatrics and Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, United States.
¹⁵ Department of Information Services, Texas Children's Hospital, Houston, TX, United States.
¹⁶ Pharming Healthcare, Inc., Warren, NJ, United States.
¹⁷ Department of Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Division of Allergy-Immunology Carilion Clinic, Roanoke, VA, United States.

Abstract

Introduction: Patients with activated PI3Kδ syndrome (APDS) may elude diagnoses for nearly a decade. Methods to hasten the identification of these patients, and other patients with inborn errors of immunity (IEIs), are needed. We sought to demonstrate that querying electronic health record (EHR) systems by aggregating disparate signs into a risk score can identify these patients.

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 patients. The query calculated an "APDS Score," which stratified risk for APDS for all individuals in these systems. Scores for all known patients with APDS (n=46) were compared.

Results: The query identified all but one known patient with APDS (98%; 45/46) as well as patients with other complex disease. Median score for all patients with APDS was 9 (IQR = 5.75; range 1-25). Sensitivity analysis suggested an optimal cutoff score of 7 (sensitivity = 0.70).

Conclusion: Disease-specific queries are a relatively simple method to foster patient identification 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.

Keywords: AI; APDS; EHR query; IEI diagnosis; diagnostic delay; inborn errors of immunity.

MeSH terms

Class I Phosphatidylinositol 3-Kinases* / genetics
Electronic Health Records*
Humans
Primary Immunodeficiency Diseases* / diagnosis

Substances

Class I Phosphatidylinositol 3-Kinases

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The authors declare that this study received funding from Pharming Healthcare, Inc. The funder was not involved in the study design, collection, analysis, interpretation of data, the original writing of this article, or the decision to submit it for publication.