Preterm Birth (PTB) is a major long-lasting public health problem being the leading cause of mortality and long-term disabilities among neonates, with heavy emotional and financial consequences to families and society. Prediction of PTB risk has been an exceedingly challenging problem, in particular for first time mothers (nulliparous women) due to the lack of prior pregnancy history. Most studies to date have examined individual risk factors, genetic, environmental, or behavioral, through univariate analyses of their association with PTB, including GWAS identifying modest contribution of common variants across six gene regions. The challenge of improving PTB prediction is due to the inherent complexity of its multifactorial etiology and the lack of approaches capable of integrating and interpreting large multidisciplinary data. Our previous work [NSF Eager 1454855, 1454814] developed predictive models for PTB based on non-genetic maternal attributes. An important question is to know whether factors other than history of PTB can be used to identify a nullipara patient at risk. We plan on devising longitudinal risk prediction methods for PTB that integrate every piece of available data. We will address three important gaps in current literature as our three project objectives: a focused study of nulliparous women and their risk for PTB; combining genetic factors with other clinical factors to determine risk ; and using longitudinal data and models to optimize scheduling of patient visits, testing and treatment. We will focus on a recently released NIH-NICHD dataset called nuMoM2b, which is a prospective cohort study of a racially/ethnically/geographically diverse population of10 ,038 nulliparous women with singleton gestation .
Our aims are as follows: (1) Longitudinal Preterm Birth Prediction ; (2) Combining clinical and genetic features for risk prediction ; (3) Assessing the effectiveness of the methods in clinical practice.

Public Health Relevance

. Over 26 billion dollars are spent annually on the delivery and care of the 12% of infants who are born preterm in the United States. A crucial challenge is to identify women who are at the highest risk for early preterm birth and to develop interventions. Equally important, would be the ability to identify women at the lowest risk to avoid unnecessary and costly interventions. Our project has the potential to advance knowledge about this long-lasting public health problem.

National Institute of Health (NIH)
National Library of Medicine (NLM)
Research Project (R01)
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Special Emphasis Panel (ZRG1)
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Sim, Hua-Chuan
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Columbia University (N.Y.)
Engineering (All Types)
Biomed Engr/Col Engr/Engr Sta
New York
United States
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