An algorithm to identify patients aged 0–3 with rare genetic disorders

• Published in: Orphanet journal of rare diseases Vol. 19; no. 1; pp. 183 - 8
• Main Authors: Webb, Bryn D., Lau, Lisa Y., Tsevdos, Despina, Shewcraft, Ryan A., Corrigan, David, Shi, Lisong, Lee, Seungwoo, Tyler, Jonathan, Li, Shilong, Wang, Zichen, Stolovitzky, Gustavo, Edelmann, Lisa, Chen, Rong, Schadt, Eric E., Li, Li
• Format: Journal Article
• Language: English
• Published: London BioMed Central 02.05.2024; BioMed Central Ltd; BMC
• Subjects: Algorithm; Algorithms; Child, Preschool; Children; Clinical decision-making; Digital phenotyping; Electronic Health Records; Female; Genetic Diseases, Inborn - diagnosis; Genetic Diseases, Inborn - genetics; Genetic screening; Health aspects; Human Genetics; Humans; Infant; Infant, Newborn; Male; Medical records; Medicine; Medicine & Public Health; Methods; Pediatric genetic disorders; Pharmacology/Toxicology; Phenotype; Rare Diseases - diagnosis; Rare Diseases - genetics; United States; Digital phenotyping; Pediatric genetic disorders; Algorithm; Clinical decision-making
• ISSN: 1750-1172; 1750-1172
• DOI: 10.1186/s13023-024-03188-9

Background With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. We sought to develop a digital phenotyping algorithm ( PheIndex ) using electronic medical records to identify children aged 0–3 diagnosed with genetic disorders or who present with illness with an increased risk for genetic disorders. Results Through expert opinion, we established 13 criteria for the algorithm and derived a score and a classification. The performance of each criterion and the classification were validated by chart review. PheIndex identified 1,088 children out of 93,154 live births who may be at an increased risk for genetic disorders. Chart review demonstrated that the algorithm achieved 90% sensitivity, 97% specificity, and 94% accuracy. Conclusions The PheIndex algorithm can help identify when a rare genetic disorder may be present, alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist.