Dataset Information

A model for prediction of spontaneous preterm birth in asymptomatic women.

ABSTRACT:

Background

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.

Methods

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.

Results

The proportion of patients with spontaneous preterm birth was 18.4% (96 of 522) at <37 weeks gestation, 5.4% (28 of 522) at <34 weeks gestation, and 2.7% (14 of 522) at <32 weeks gestation. The match rates with the combination of demographic, clinical, and genetic factors using a Bayesian filtering method (PopBayes) were higher than the match rates using demographic and clinical factors only, including maternal age, maternal body mass index (BMI), prior preterm birth, education, occupation, income, and active and passive smoking. The match rates in preterm delivery before 32 weeks of gestation were higher than the match rates in preterm delivery before 37 and 34 weeks of gestation (94.3% vs. 84.7% and 82.0%, respectively). The negative predictive values for demographic, clinical, and genetic factors in predicting preterm delivery using PopBayes were consistently >90%.

Conclusions

We suggest that Bayesian filtering (PopBayes) is a customizable and useful tool in establishing a model for the prediction of preterm birth.

SUBMITTER: Lee KA

PROVIDER: S-EPMC3236991 | biostudies-literature |

REPOSITORIES: biostudies-literature

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