Detecting moderate or complex congenital heart defects in adults from an electronic health records system.

Abstract

The prevalence of moderate or complex (moderate-complex) congenital heart defects (CHDs) among adults is increasing due to improved survival, but many patients experience lapses in specialty care or their CHDs are undocumented in the medical system. There is, to date, no efficient approach to identify this population. To develop and assess the performance of a risk score to identify adults aged 20-60 years with undocumented specific moderate-complex CHDs from electronic health records (EHR). We used a case-control study (596 adults with specific moderate-complex CHDs and 2384 controls). We extracted age, race/ethnicity, electrocardiogram (EKG), and blood tests from routine outpatient visits (1/2009 through 12/2012). We used multivariable logistic regression models and a split-sample (4: 1 ratio) approach to develop and internally validate the risk score, respectively. We generated receiver operating characteristic (ROC) c-statistics and Brier scores to assess the ability of models to predict the presence of specific moderate-complex CHDs. Out of six models, the non-blood biomarker model that included age, sex, and EKG parameters offered a high ROC c-statistic of 0.96 [95% confidence interval: 0.95, 0.97] and low Brier score (0.05) relative to the other models. The adult moderate-complex congenital heart defect risk score demonstrated good accuracy with 96.4% sensitivity and 80.0% specificity at a threshold score of 10. A simple risk score based on age, sex, and EKG parameters offers early proof of concept and may help accurately identify adults with specific moderate-complex CHDs from routine EHR systems who may benefit from specialty care.