ArticleCommunications medicine2025
Associations of maternal and paternal characteristics with cardiac malformations using real-world data and machine learning.
Article in Communications medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Update of
Authors and funding
6 authors.
Funding
Abstract
backgroundEffective 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.
methodsAmong 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.
resultsAt 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.
conclusionsScreening 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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.