Piloting an automated query and scoring system to facilitate APDS patient identification from health systems
IntroductionPatients 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) syste...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1508780/full |
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| author | 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 |
| author_facet | 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 |
| author_sort | Amy M. FitzPatrick |
| collection | DOAJ |
| description | IntroductionPatients 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.MethodsWe 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.ResultsThe 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).ConclusionDisease-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. |
| format | Article |
| id | doaj-art-3282cdca75d341ea904d234dba81d956 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-3282cdca75d341ea904d234dba81d9562025-01-21T05:43:19ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.15087801508780Piloting an automated query and scoring system to facilitate APDS patient identification from health systemsAmy M. FitzPatrick0Aaron T. Chin1Sharon Nirenberg2Charlotte Cunningham-Rundles3Keith Sacco4Jesse Perlmutter5Joseph F. Dasso6Athanasios Tsalatsanis7Jay Maru8Jessica Creech9Jolan E. Walter10Nicholas Hartog11Neema Izadi12Mandy Palmucci13Manish J. Butte14Klaus Loewy15Anurag Relan16Nicholas L. Rider17Precision AQ, Bethesda, MD, United StatesDepartment of Pediatrics, University of California, Los Angeles, Los Angeles, CA, United StatesDivision of Informatics and Data Architecture, Icahn School of Medicine, Departments of Scientific Computing and Data, Mount Sinai School of Medicine, New York, NY, United StatesDivision of Clinical Immunology, Icahn School of Medicine, Departments of Medicine and Pediatrics, Mount Sinai School of Medicine, New York, NY, United StatesDepartment of Child Health, University of Arizona College of Medicine and Division of Pulmonology, Section of Allergy-Immunology, Phoenix Children’s Hospital, Phoenix, AZ, United StatesPhoenix Children’s Hospital, Phoenix, AZ, United StatesDepartment of Pediatric Allergy and Immunology, University of South Florida at Johns Hopkins All Children’s Hospital, St. Petersburg, FL, United StatesResearch Methodology and Biostatistics Core, Morsani College of Medicine, University of South Florida Health, St. Petersburg, FL, United StatesManagement Analyst, Research Methodology and Biostatistics Core, Morsani College of Medicine, University of South Florida Health, St. Petersburg, FL, United States0Department of Pediatrics, University of South Florida at Johns Hopkins All Children’s Hospital, St. Petersburg, FL, United StatesDepartment of Pediatric Allergy and Immunology, University of South Florida at Johns Hopkins All Children’s Hospital, St. Petersburg, FL, United States1Division 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 States2Division of Clinical Immunology and Allergy, Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, United States3Division of Information Services, Children’s Hospital Los Angeles, University of Southern California, Los Angeles, CA, United States4Department of Pediatrics and Department of Microbiology, Immunology, and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, United States5Department of Information Services, Texas Children’s Hospital, Houston, TX, United States6Pharming Healthcare, Inc., Warren, NJ, United States7Department of Health Systems & Implementation Science, Virginia Tech Carilion School of Medicine, Division of Allergy-Immunology Carilion Clinic, Roanoke, VA, United StatesIntroductionPatients 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.MethodsWe 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.ResultsThe 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).ConclusionDisease-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.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1508780/fullAPDSEHR queryAIinborn errors of immunitydiagnostic delayIEI diagnosis |
| spellingShingle | 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 Piloting an automated query and scoring system to facilitate APDS patient identification from health systems Frontiers in Immunology APDS EHR query AI inborn errors of immunity diagnostic delay IEI diagnosis |
| title | Piloting an automated query and scoring system to facilitate APDS patient identification from health systems |
| title_full | Piloting an automated query and scoring system to facilitate APDS patient identification from health systems |
| title_fullStr | Piloting an automated query and scoring system to facilitate APDS patient identification from health systems |
| title_full_unstemmed | Piloting an automated query and scoring system to facilitate APDS patient identification from health systems |
| title_short | Piloting an automated query and scoring system to facilitate APDS patient identification from health systems |
| title_sort | piloting an automated query and scoring system to facilitate apds patient identification from health systems |
| topic | APDS EHR query AI inborn errors of immunity diagnostic delay IEI diagnosis |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1508780/full |
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