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

Autor: Webb BD; Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. bdwebb@wisc.edu.; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA. bdwebb@wisc.edu., Lau LY; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Tsevdos D; Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Shewcraft RA; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Corrigan D; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Shi L; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Lee S; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Tyler J; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Li S; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Wang Z; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Stolovitzky G; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Edelmann L; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Chen R; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA., Schadt EE; Department of Genetics and Genomic Sciences, The Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Li L; GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA. dlleely@gmail.com.
Jazyk: angličtina
Zdroj: Orphanet journal of rare diseases [Orphanet J Rare Dis] 2024 May 02; Vol. 19 (1), pp. 183. Date of Electronic Publication: 2024 May 02.
DOI: 10.1186/s13023-024-03188-9
Abstrakt: 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.
(© 2024. The Author(s).)
Databáze: MEDLINE
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