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Systematic method for classifying multiple congenital anomaly cases in electronic health records
Elly Brokamp1, Tyne Miller-Fleming1, Alexandra Scalici1
1Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN; Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN.
Insights
A new method accurately identifies individuals with multiple congenital anomalies (MCA) in electronic health records (EHRs). This approach improves understanding of MCA
Area of Science:
- Medical informatics
- Genetics
- Pediatrics
Background:
- Congenital anomalies (CAs) impact 3% of births, causing significant infant morbidity and mortality.
- Multiple congenital anomalies (MCA) occur in some individuals, but systematic identification in electronic health records (EHRs) is lacking.
- Understanding MCA's genetic and epidemiologic factors is crucial but hindered by identification challenges.
Purpose of the Study:
- To develop a scalable and accurate method for identifying individuals with multiple congenital anomalies (MCA) within electronic health records (EHRs).
- To enable improved research into the genetic and epidemiologic underpinnings of MCA.
- To establish a standardized approach for MCA characterization across diverse EHR databases.
Main Methods:
- Evaluation of three distinct MCA classification approaches using an anonymized EHR database.
- Implementation of a novel method that bypasses minor vs. major CA differentiation.
- Utilizing phenome-wide association studies to analyze the phenome associated with previously classified minor CAs.
Main Results:
- The developed universal MCA identification method in EHRs demonstrates high accuracy (97.1% PPV).
- Identified MCA cases show increased hospital utilization, with 41% receiving inpatient care.
- The method effectively captures detailed patterns of congenital anomalies and was validated in two additional cohorts.
Conclusions:
- A comprehensive method for identifying individuals with MCA in EHRs has been successfully developed.
- This method facilitates deeper investigation into the genetic causes of MCA.
- The approach is transferable and applicable to EHR systems utilizing billing codes.
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.
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