Use of Real-World Data and Machine Learning to Screen for Maternal and Paternal Characteristics Associated with Cardiac Malformations

TL;DR

This study uses real-world data and machine learning to identify maternal and paternal characteristics linked to cardiac malformations in infants.

Contribution

The novel use of machine learning and real-world data to screen for potential risk factors of cardiac malformations.

Findings

Maternal diabetes and chronic hypertension were confirmed as risk factors for cardiac malformations.

Less recognized signals included maternal use of fingolimod or azathioprine.

Screening methods successfully identified known risk factors, suggesting potential for discovering new ones.

Abstract

Effective prevention of cardiac malformations, a leading cause of infant morbidity, is constrained by limited understanding of etiology. The study objective was to screen for associations between maternal and paternal characteristics and cardiac malformations. We selected 720,381 pregnancies linked to live-born infants (n=9,076 cardiac malformations) in 2011–2021 MarketScan US insurance claims data. Odds ratios were estimated with clinical diagnostic and medication codes using logistic regression. Screening of 2,000 associations selected 81 associated codes at the 5% false discovery rate. Grouping of selected codes, using latent semantic analysis and the Apriori-SD algorithm, identified elevated risk with known risk factors, including maternal diabetes and chronic hypertension. Less recognized potential signals included maternal fingolimod or azathioprine use. Signals identified might be explained by confounding, measurement error, and selection bias and warrant further investigation. The screening methods employed identified known risk factors, suggesting potential utility for identifying novel risk factors for other pregnancy outcomes.

Figures