Biological Markers for the Prediction of the "Great Obstetrical Syndromes": A Longitudinal Study

Brief Summary

Detailed Description

Objective: To study the natural history of normal pregnancy and the most frequent pregnancy complications responsible for the excessive rate of perinatal morbidity and mortality, in order to develop models to predict the occurrence of these complications of pregnancy at the earliest possible time. The study focuses on the prediction of preterm labor with intact membranes, preterm prelabor rupture of membranes (PROM), preeclampsia, small for gestational age, gestational diabetes, and fetal death. These complications account for a minimum of $30 billion annually in the US alone. Study population: A cohort of pregnant women seeking care at the prenatal clinic of the Perinatology Research Branch in Detroit, Michigan. Design: A prospective observational cohort study of the natural history of women with a normal pregnancy, a history of adverse outcome, or those with a complication in the index pregnancy; therefore, this study will include nulliparous and parous women. Data will be collected at the time of clinic visits and will include interviews, clinical measurements, and ultrasound studies. We will assemble a biorepository of maternal biological fluids (blood, urine, saliva, cervicovaginal fluid, gingival crevicular fluid, swabs to characterize microbiota, amniotic fluid when a clinically indicated amniocentesis is performed). Placentas will be collected at the time of delivery as well as umbilical blood, and swabs to characterize the neonatal microbiota. We will use a retrospective case control and case-cohort design to generate models for the prediction of the most common pregnancy complications. These models will be developed by classifying obstetrical complications according to clinical presentation and histologic placental lesions. Models will be developed and subsequently validated in an independent cohort. Outcome measures: The goal is to develop sensitive, specific, and parsimonious predictive models to identify the patients at risk for developing complications of pregnancy using a combination of clinical and biological markers (biochemical and biophysical).

Primary Outcomes