Title: Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression
Authors: Gevaert, Olivier ×
De Smet, Frank
Kirk, E
Van Calster, Ben
Bourne, Tom
Van Huffel, Sabine
Moreau, Yves
Timmerman, Dirk
De Moor, Bart
Condous, G #
Issue Date: Jul-2006
Publisher: Published for the European Society of Human Reproduction and Embryology by IRL Press
Series Title: Human Reproduction vol:21 issue:7 pages:1824-1831
Abstract: BACKGROUND: As women present at earlier gestations to early pregnancy units (EPUs), the number of women diagnosed with a pregnancy of unknown location (PUL) increases. Some of these women will have an ectopic pregnancy (EP), and it is this group in the PUL population that poses the greatest concern. The aim of this study was to develop Bayesian networks to predict EPs in the PUL population. METHODS: Data were gathered in a single EPU from all women with a PUL. This data set was divided into a model-building (599 women with 44 EPs) and a validation (257 women with 22 EPs) data set and consisted of the following variables: vaginal bleeding, fluid in the pouch of Douglas, midline echo, lower abdominal pain, age, endometrial thickness, gestation days, the ratio of HCG at 48 and 0 h, progesterone levels (0 and 48 h) and the clinical outcome of the PUL. We developed Bayesian networks with expert information using this data set to predict EPs. RESULTS: The best Bayesian network used the gestational age, HCG ratio and the progesterone level at 48 h and had an area under the receiver operator characteristic curve (AUC) of 0.88 for predicting EPs when tested prospectively. CONCLUSIONS: Discrete-valued Bayesian networks are more complex to build than, for example, logistic regression. Nevertheless, we have demonstrated that such models can be used to predict EPs in a PUL population. Prospective interventional multicentre studies are needed to validate the use of such models in clinical practice.
ISSN: 0268-1161
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
Vesalius Research Centre (-)
Basic Research in Gynaecology Section (-)
Screening, Diagnostics and Biomarkers (-)
Environment and Health - miscellaneous
× corresponding author
# (joint) last author

Files in This Item:

There are no files associated with this item.

Request a copy


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science