Phenotype driven molecular genetic test recommendation for diagnosing pediatric rare disorders

Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early use of exome sequencing...

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Bibliographic Details
Published inNPJ digital medicine Vol. 7; no. 1; pp. 333 - 12
Main Authors Chen, Fangyi, Ahimaz, Priyanka, Nguyen, Quan M., Lewis, Rachel, Chung, Wendy K., Ta, Casey N., Szigety, Katherine M., Sheppard, Sarah E., Campbell, Ian M., Wang, Kai, Weng, Chunhua, Liu, Cong
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 21.11.2024
Nature Publishing Group
Nature Portfolio
Subjects
692/700/139/1512
692/700/1720/3187
Biomedicine
Biotechnology
Demographics
Electronic health records
Genetic disorders
Genetic testing
Genetics
Genomes
Genotype & phenotype
Hospitals
Machine learning
Medical diagnosis
Medicine
Medicine & Public Health
Metabolism
Nervous system
Patients
Pediatrics
Rare diseases
Whole genome sequencing
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ISSN2398-6352
2398-6352
DOI10.1038/s41746-024-01331-1

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Summary:Patients with rare diseases often experience prolonged diagnostic delays. Ordering appropriate genetic tests is crucial yet challenging, especially for general pediatricians without genetic expertise. Recent American College of Medical Genetics (ACMG) guidelines embrace early use of exome sequencing (ES) or genome sequencing (GS) for conditions like congenital anomalies or developmental delays while still recommend gene panels for patients exhibiting strong manifestations of a specific disease. Recognizing the difficulty in navigating these options, we developed a machine learning model trained on 1005 patient records from Columbia University Irving Medical Center to recommend appropriate genetic tests based on the phenotype information. The model achieved a remarkable performance with an AUROC of 0.823 and AUPRC of 0.918, aligning closely with decisions made by genetic specialists, and demonstrated strong generalizability (AUROC:0.77, AUPRC: 0.816) in an external cohort, indicating its potential value for general pediatricians to expedite rare disease diagnosis by enhancing genetic test ordering.
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ISSN:2398-6352
2398-6352
DOI:10.1038/s41746-024-01331-1