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An algorithm to identify patients aged 0-3 with rare genetic disorders
Bryn D Webb1,2, Lisa Y Lau3, Despina Tsevdos4
1Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. bdwebb@wisc.edu.
Insights
A new algorithm, PheIndex, uses electronic health records to identify children at risk for rare genetic disorders. This tool aids in early diagnosis and genetic testing referrals for pediatric patients.
Area of Science:
- Pediatric rare diseases
- Clinical informatics
- Genomic medicine
Background:
- Identifying rare genetic disorders in children is difficult due to incomplete electronic health records and coding inaccuracies.
- Over 7000 Mendelian disorders exist, presenting diagnostic challenges in early childhood.
- There is a need for improved methods to detect genetic conditions in young children.
Purpose of the Study:
- To develop and validate a digital phenotyping algorithm (PheIndex) for identifying children aged 0-3 at risk for genetic disorders.
- To leverage electronic medical record data for early detection of rare genetic conditions.
- To improve the diagnostic pathway for pediatric patients with potential genetic disorders.
Main Methods:
- Developed the PheIndex algorithm based on 13 criteria derived from expert opinion.
- Utilized electronic medical records to identify children aged 0-3 with potential genetic disorder diagnoses or risks.
- Validated algorithm performance through comprehensive chart review.
Main Results:
- The PheIndex algorithm identified 1,088 children at increased risk for genetic disorders out of 93,154 live births.
- Chart review confirmed the algorithm's high performance: 90% sensitivity, 97% specificity, and 94% accuracy.
- The algorithm successfully flagged children requiring further genetic evaluation.
Conclusions:
- The PheIndex algorithm effectively identifies children who may have a rare genetic disorder.
- This tool can prompt healthcare providers to consider diagnostic genetic testing or referral to a medical geneticist.
- PheIndex enhances the early detection and management of genetic conditions in pediatric populations.
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.
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