title: Predicting the outcome of pregnancies of unknown location: Bayesian networks with expert prior information compared to logistic regression.
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.
authors.affiliation: Department of Electrical Engineering ESAT-SCD, Katholieke Universiteit Leuven, Kasteelpark Arenberg, Leuven, Belgium, and Early Pregnancy, Gynecology Ultrasound and MAS Unit, St George's Hospital Medical School, London, UK. olivier.gevaert@esat.kuleuven.be
authors.forename: O
authors.initials: O
authors.lastname: Gevaert
authors.forename: F
authors.initials: F
authors.lastname: De Smet
authors.forename: E
authors.initials: E
authors.lastname: Kirk
authors.forename: B
authors.initials: B
authors.lastname: Van Calster
authors.forename: T
authors.initials: T
authors.lastname: Bourne
authors.forename: S
authors.initials: S
authors.lastname: Van Huffel
authors.forename: Y
authors.initials: Y
authors.lastname: Moreau
authors.forename: D
authors.initials: D
authors.lastname: Timmerman
authors.forename: B
authors.initials: B
authors.lastname: De Moor
authors.forename: G
authors.initials: G
authors.lastname: Condous
mesh_terms: D000293:Adolescent; D000328:Adult; D001499:Bayes Theorem; D006063:Chorionic Gonadotropin / Q000097:blood; D005260:Female; D005865:Gestational Age; D006801:Humans; D016015:Logistic Models; D011237:Predictive Value of Tests; D011247:Pregnancy; D011256:Pregnancy Outcome; D011271:Pregnancy, Ectopic; D011446:Prospective Studies
chemical_list: D006063:Chorionic Gonadotropin