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

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...

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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
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ISSN	1750-1172 1750-1172
DOI	10.1186/s13023-024-03188-9

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Abstract	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.
AbstractList	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. 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.BACKGROUNDWith 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.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.RESULTSThrough 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.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.CONCLUSIONSThe 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. 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. 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. 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. 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. Keywords: Digital phenotyping, Algorithm, Pediatric genetic disorders, Clinical decision-making 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. 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. 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. Abstract 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.
ArticleNumber	183
Audience	Academic
Author	Li, Li Tsevdos, Despina Tyler, Jonathan Stolovitzky, Gustavo Corrigan, David Webb, Bryn D. Shi, Lisong Chen, Rong Schadt, Eric E. Lau, Lisa Y. Shewcraft, Ryan A. Wang, Zichen Lee, Seungwoo Li, Shilong Edelmann, Lisa
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Keywords	Digital phenotyping Pediatric genetic disorders Algorithm Clinical decision-making
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Snippet	Background With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical... With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical record data is... Background With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic medical... Abstract Background With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured electronic...
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StartPage	183
SubjectTerms	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
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Title	An algorithm to identify patients aged 0–3 with rare genetic disorders
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