Wednesday, January 15, 2025
1/15/2025
PROJECT SUMMARY/ABSTRACT In the United States, over 60,000 infants are born very preterm (VPT, ≤32 weeks’ gestation) each year; more than 15% of these infants die in infancy, while ~20% survive with severe morbidity. Despite the sobering statistics, not all preterm infants face these consequences. A crucial factor in determining outcomes is the individual infant's response to any given therapy. However, our understanding of factors causing variation in response, beyond immaturity itself, is limited and impedes our ability to customize therapies. The perinatal period, is a critical time window when multiple changes and adaptations in the fetus/newborn set the foundation for the future. Perinatal events could therefore be important determinants of variation in later life health and could be used to identify therapeutically relevant phenotypes. Previous phenotyping efforts in preterm birth have either based the categorization on the presentation leading to preterm delivery (aimed at prevention of preterm birth); or used neonatal severity of illness markers (aimed at mortality prediction); or made prediction models for specific complications (aimed at improving management of said morbidity). These approaches intentionally exclude perinatal interventions such as delivery mode and perinatal antibiotics to minimize impact of practice variation. However, maternal care practices are critical exposures that affect the infant’s response to interventions. Our central hypothesis is that perinatal variables combining reason for birth with intrapartum care variables and neonatal presentation can be used to categorize infants into finite ‘phenotypes’ early in life and can explain variation in multiple later health outcomes. In aim 1, we will use a novel conceptual model to create perinatal phenotypes by integrating the variables using clustering techniques. In aim 2 and 3, we will determine the contribution of perinatal variables in explaining the variation of 2 key outcomes: timing and type of late-onset infection and abnormal growth trajectory. In experimental models, both late-onset infection and weight gain are causally linked with early-life colonization, which, in turn, is influenced by multiple perinatal factors. Thus, perinatal factors, and any derived phenotypes from these factors, may explain the risk variation for these outcomes. To perform this study, we will leverage a unique registry of over 3600 very preterm infants and their mothers with manually adjudicated reason for birth and in-patient neonatal course, that we can link with longitudinal pediatric records from birth to 5 years. The study team and PI have extensive experience in modeling perinatal variables, creating linked datasets, clustering techniques and expertise the field of preterm birth and neonatal infections. The expected result of our study is to create a much-needed and novel patient selection framework for early life interventions, specifically those targeting early colonization, infection and inflammation. If successful, such a framework could change the paradigm of how trials are designed and ultimately how clinical care is customized.
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