An algorithm to identify patients with rare genetic disorders and its real-world data application

Objectives Develop a digital phenotyping algorithm (PheIndex) using electronic medical records (EMR) data to identify children aged 0-3 who have been diagnosed with genetic disorders or present with illness with an increased risk for genetic disorders from a mother-child cohort. Methods We established 13 criteria for the algorithm where two metrics – a quantified score and a classification – were derived. The criteria and the classification were validated by chart review from a pediatrician and clinical geneticist. To demonstrate the utility of our algorithm in real-world evidence applications, we examined the association between size of carrier screening panel (small/≤4 genes [CS-S] vs large/≥100genes [CS-L]) undertaken by mothers prior to delivery, and children classified as presenting with illness with an increased risk for genetic disorders by our algorithm. Results The PheIndex algorithm identified 1,088 such children out of 93,154 live births and achieved 90% sensitivity, 97% specificity, and 94% accuracy by chart review. We found that children whose mothers received CS-L were less likely to be classified as presenting with illness with an increased risk for genetic disorders and a decreased need to have multiple specialist visits and multiple ER visits, compared to children whose mothers received CS-S. Conclusions The PheIndex algorithm can help identify when a rare genetic disorder may be present, and has the potential to improve healthcare delivery by alerting providers to consider ordering a diagnostic genetic test and/or referring a patient to a medical geneticist or other specialists. Article Summary Algorithm using EMR data to identify children who have been diagnosed with a genetic disorder or present with illness with increased risk of genetic disorders. What’s known on this subject With over 7000 Mendelian disorders, identifying children with a specific rare genetic disorder diagnosis through structured EMR data is challenging given incompleteness of records, inaccurate medical diagnosis coding, as well as heterogeneity in clinical symptoms and procedures for specific disorders. What this study adds We developed a digital phenotyping algorithm using electronic medical records (EMR) data to identify children aged 0-3 who have been diagnosed with genetic disorders or present with illness with an increased risk for genetic disorders from a mother-child cohort.