Communications medicine · 2025
Associations of maternal and paternal characteristics with cardiac malformations using real-world data and machine learning.
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Brown Jeremy P, Huybrechts Krista F, Straub Loreen, Heider Dominik, Bateman Brian T, Hernández-Díaz Sonia
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.