Systematic method for classifying multiple congenital anomaly cases in electronic health records

作者：Elly Brokamp, Tyne W. Miller‐Fleming, Alexandra Scalici, Gillian W. Hooker, Rizwan Hamid, Digna R. Velez Edwards, Wendy K. Chung, Yuan Luo, Krzysztof Kiryluk, Nita A Limidi, Nikhil K. Khankari, Nancy J. Cox, Lisa Bastarache, Megan M. Shuey · 发表于：Genetics in Medicine · 年份：2025 · DOI：10.1016/j.gim.2025.101415 · 被引用次数：1 · 研究领域：Genomics and Rare Diseases、Genetic Associations and Epidemiology、BRCA gene mutations in cancer

PURPOSE: Congenital anomalies (CAs) affect approximately 3% of live births and are the leading cause of infant morbidity and mortality. Many individuals have multiple CAs (MCA), a constellation of 2 or more unrelated CAs; yet, there is no consensus on how to systematically identify these individuals in electronic health records (EHRs). We developed a scalable method to characterize MCA in the EHR, allowing for the dramatic improvement of our understanding of the genetic and epidemiologic underpinnings of MCA. METHODS: From the Vanderbilt University Medical Center's anonymized EHR database, we evaluated 3 different approaches for classifying MCA, including a novel approach that removed minor vs major differentiation and their associated clinical utilization and population characteristics. Using phenome-wide association studies, we assessed the phenome associated with previously classified minor CAs. RESULTS: Our proposed universal method for MCA identification in the EHR is accurate (positive predictive value = 97.1%), associated with heightened hospital utilization (41% receiving inpatient care), and captures granular patterns of CAs. A secondary application of our method was done in 2 separate cohorts. CONCLUSION: We developed a method to comprehensively identify individuals with MCA in the EHR, allowing researchers to better investigate the genetic etiologies of MCA. This method can be applied across EHR databases with billing codes.