Goal
To develop predictive models for risk of preterm birth in pregnant individuals based on vaginal microbiome data.
There is a controversy whether the vaginal microbiome of pregnant patients differs between patients who deliver term and those who deliver preterm. (1–4) Our challenge seeks to use crowd-sourcing to explore such differences and determine if computational models using vaginal microbiome data will enable improved prediction of preterm birth in order to benefit those who suffer from this condition.
Motivation
Globally, about 11% of infants every year are born preterm, defined as birth prior to 37 weeks of gestation, totaling nearly 15 million births.(5) In addition to the emotional and financial toll on families, preterm births have higher rates of neonatal death, nearly 1 million deaths each year, and long-term health consequences for some children. Infants born preterm are at risk for a variety of adverse outcomes, such as respiratory illnesses, cerebral palsy, infections, and blindness, with infants born very preterm (i.e., before 32 weeks) at increased risk of these conditions.(6)
The ability to accurately predict which women are at a higher risk for preterm birth would help healthcare providers to treat in a timely manner those at higher risk of delivering preterm. Currently available treatments for pregnant women at risk of preterm delivery include corticosteroids for fetal maturation and magnesium sulfate provided prior to 32 weeks to prevent cerebral palsy.(7)
There are several factors known to be associated with PTB, including maternal age, body mass index (BMI), education, smoking, history of PTB, a short cervix, and genetic polymorphisms.(8–11) Nevertheless, there are currently no clinical tools that enable the early and reliable prediction of preterm birth.(12)
There is some indication that the vaginal microbiome plays a significant role in adverse pregnancy outcomes, specifically preterm birth. Previous studies have shown that there are significant differences between the vaginal microbiome of patients who deliver at term and those who deliver prematurely. Vaginal microbiomes with increased diversity as well as communities where Lactobacillus is not dominant have been associated with PTB.(13–15) We hypothesize that this data could be used as a potential avenue for predicting which women are at a higher risk delivering birth.
Patients and healthcare systems alike would benefit from the formation of a precise determination of risk for PTB as outlined in the Challenge.
