Abstract 13374: The Intersection of Prenatal Clinical and Demographic Factors in Predicting Fetal Brain Volume and Neurodevelopmental Outcome in Single Ventricle CHD

Abstract

Background: Fetuses with single ventricle (SV) CHD have small total brain volumes (TBV) which has been linked to neurodevelopmental impairment (NDI). Evidence suggests a prenatal origin of NDI for CHD. Our primary aim was to use machine learning methods to identify prenatal variables that predict fetal brain growth and in turn early NDI. Methods: This is a multi-center prospective cohort study of maternal-infant dyads with a fetal diagnosis of SV who underwent fetal brain MRI and in-depth prenatal clinical and sociodemographic data collection (self reported race/ethnicity, income, education, area deprivation index, maternal health, genetics, placental health). Infants had ND testing at 12-30 months with Bayley Scales of Infant and Toddler Development. Machine learning models used recursive partitioning to predict 1) gestational-age (GA) and sex-adjusted fetal TBV and 2) ND outcome while including fetal TBV in the models. Results: A total of 67 maternal-infant dyads were enrolled with a mean GA of 33.4 weeks (SD=3.4) at fetal MRI and male predominance (57%); 48 infants had follow-up at median age of 23.5 months (SD=6.3). Key predictors of fetal brain volume were race/ethnicity, maternal age, and cardiac anatomy (Figure 1A). For NDI, fetal brain volume was the strongest predictor of motor outcomes but was not associated with other domains (Figure 1D). Cardiac anatomy was the strongest predictor of cognitive outcome (Figure 1B), while race/ethnicity was the strongest predictor of language (Figure 1C). Cardiac anatomy, maternal age, and cerebral and placental hemodynamics (e.g., middle cerebral or umbilical artery pulsatility) each predicted multiple domains of neurodevelopment. Conclusions: In regression tree modeling, sociodemographic variables are the strongest predictors of fetal TBV, which in turn predicts motor outcome. Prenatal factors predicting NDI across all domains span sociodemographic, cardiac, and placental characteristics.