ArticleGenetics in medicine : official journal of the American College of Medical Genetics2025
Systematic method for classifying multiple congenital anomaly cases in electronic health records.
Article in Genetics in medicine : official journal of the American College of Medical Genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeCongenital 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.
methodsFrom 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.
resultsOur 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.
conclusionWe 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.
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