Systematic Method for Classifying Multiple Congenital Anomaly Cases in Electronic Health Records.

Purpose: Congenital anomalies (CAs) affect ∼3% of live births and are the leading cause of infant morbidity and mortality. Many individuals have multiple congenital anomalies (MCA), a constellation of two or more unrelated CAs, yet there is no consensus on how to systematically identify these individuals in electronic health records (EHR). We developed a scalable method to characterize MCA in the EHR, allowing for the dramatic improvement of our understanding of the genetic and epidemiological underpinnings of MCA.

Methods: From Vanderbilt University Medical Center's anonymized EHR database, we evaluated three 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 (PPV= 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 two 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.