A model for prediction of spontaneous preterm birth in asymptomatic women

Preterm birth is a complex health problem with social, environmental, behavioral, and genetic determinants of an individual's risk and remains a major challenge in obstetrics. Recent research has caused improvements in predicting preterm birth; however, there is still controversy about the prediction of preterm birth in asymptomatic women. The purpose of this study was to determine if Bayesian filtering can be used in a clinical setting to predict spontaneous preterm birth in asymptomatic women. A model of predicting spontaneous preterm birth using PopBayes based on a Bayesian filtering algorithm was developed using a previously collected dataset, then applied to a prospectively collected cohort of asymptomatic women who delivered singleton live newborns at or after 24 weeks of gestation. Cases complicated with major congenital malformations were excluded. The proportion of patients with spontaneous preterm birth was 18.4% (96 of 522) at