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A model for prediction of spontaneous preterm birth in asymptomatic women

Title
A model for prediction of spontaneous preterm birth in asymptomatic women
Authors
Lee K.A.Chang M.H.Park M.-H.Park H.Ha E.H.Park E.A.Kim Y.J.
Ewha Authors
하은희박은애김영주박혜숙박미혜
SCOPUS Author ID
하은희scopus; 박은애scopus; 김영주scopus; 박혜숙scopus; 박미혜scopus
Issue Date
2011
Journal Title
Journal of Women's Health
ISSN
1540-9996JCR Link
Citation
vol. 20, no. 12, pp. 1825 - 1831
Indexed
SCI; SCIE; SSCI; SCOPUS WOS scopus
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. © 2011, Mary Ann Liebert, Inc.
DOI
10.1089/jwh.2011.2729
Appears in Collections:
의학전문대학원 > 의학과 > Journal papers
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