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
Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
GeneDx Holdings Corp, (formerly known as Sema4 Holdings Corp.), Stamford, Connecticut, CT, USA.
Orphanet J Rare Dis. 2024 May 2;19(1):183. doi: 10.1186/s13023-024-03188-9.
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.
已知有7000多种孟德尔疾病，鉴于记录不完整、医学诊断编码不准确以及特定疾病临床症状和诊疗程序的异质性，通过结构化电子病历数据识别患有特定罕见遗传疾病的儿童具有挑战性。我们试图开发一种数字表型算法（PheIndex），利用电子病历识别0至3岁被诊断患有遗传疾病或有遗传疾病风险增加的患病儿童。
通过专家意见，我们为该算法建立了13条标准，并得出一个分数和一种分类。通过病历审查验证了每条标准和分类的性能。PheIndex在93154例活产儿中识别出1088名可能有遗传疾病风险增加的儿童。病历审查表明，该算法的灵敏度达到90%，特异度达到97%，准确率达到94%。
PheIndex算法有助于识别可能存在罕见遗传疾病的情况，提醒医疗服务提供者考虑安排诊断性基因检测和/或将患者转诊给医学遗传学家。
