Associations of maternal and paternal characteristics with cardiac malformations using real-world data and machine learning

Effective prevention of cardiac malformations is constrained by limited understanding of etiology. We used 2011-2021 MarketScan US insurance claims data to identify and characterize associations between maternal and paternal characteristics and non-chromosomal cardiac malformations. Among 693,483 singleton live-birth pregnancies of women linked to infants (of which 488,146 linked to fathers), odds ratios were estimated between 2000 clinical diagnostic and medication codes (500 clinical and 500 medication codes each for mothers and fathers) and cardiac malformations (n = 7522 affected pregnancies) using logistic regression. Associations were selected using procedures to control the false discovery rate (FDR). Selected codes were grouped using latent semantic analysis alongside hierarchical clustering. At the 5% FDR, 67 codes are selected of which 63 are maternal and four paternal. Elevated risk with maternal diabetes, obesity, and chronic hypertension, highlights the importance of maternal cardiometabolic health for cardiac malformations. Additional potential signals included maternal fingolimod or azathioprine use. The relative lack of paternal associations is consistent with prior findings of few replicated associations with paternal non-genetic exposures. Screening associations, with interpretation aided by unsupervised machine learning methods, identifies, in this study, both known risk factors and potential signals. Signals might be explained by confounding, other systematic errors, or chance, and warrant further investigation. The causes of heart defects in the fetus are often unclear. Using data on pregnant women, their partners, and their infants from US medical insurance claims data, we identified and characterized associations between the clinical condition of the parents, as well as medications taken by the parents, and heart defects detected in the infant. Known risk factors, such as diabetes in the mother, were identified, in addition to potential signals for further investigation. Brown et al. apply statistical and unsupervised machine learning methods to identify and characterize associations between maternal and paternal characteristics with infant cardiac malformations. Known risk factors were identified alongside potential signals, which warrant further investigation.