A Bayesian Latent Variable Model for Identifying Slow Early Fetal Growth

• Activity Number: 224
• Type: Topic Contributed
• Date/Time: Monday, August 2, 2010 : 2:00 PM to 3:50 PM
• Sponsor: Biometrics Section
• Author(s): James C. Slaughter*+ and Amy H. Herring
• Companies: Vanderbilt University and The University of North Carolina at Chapel Hill
• Keywords: mixture ; latent ; longitudinal ; pregnancy

Abstract

Fetal growth restriction is a leading cause of perinatal morbidity and mortality that could be reduced if high-risk infants are identified early in pregnancy. We propose a Bayesian model for aggregating multiple longitudinal ultrasound measurements into underlying, latent variables. We allow the latent variables to follow a flexible mixture distribution to lessen typical Normality assumptions. Our methods are also able to separate high from low risk subjects using latent classes that do no rely on using predetermined cut-points to separate risk levels. Our results identified a latent class of subjects who have increased blood flow restriction and below average intrauterine size during pregnancy. These subjects were more likely to be growth restricted at birth than a class of individuals with typical size and blood flow.