Gevaert O, De Smet F, Kirk E, Van Calster B, Bourne T, Van Huffel S, Moreau Y, Timmerman D, De Moor B, Condous G
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.
Hum Reprod. 2006 Jul;21(7):1824-31. doi: 10.1093/humrep/del083. Epub 2006 Apr 6.
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.
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.
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.
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.
随着女性在更早孕期前往早期妊娠单元（EPU）就诊，被诊断为妊娠部位不明（PUL）的女性数量增加。这些女性中的一些会发生异位妊娠（EP），正是PUL人群中的这一群体引起了最大关注。本研究的目的是开发贝叶斯网络以预测PUL人群中的异位妊娠。
从一个EPU收集所有PUL女性的数据。该数据集被分为一个模型构建数据集（599名女性，其中44例为异位妊娠）和一个验证数据集（257名女性，其中22例为异位妊娠），并包含以下变量：阴道出血、Douglas腔积液、中线回声、下腹部疼痛、年龄、子宫内膜厚度、妊娠天数、48小时与0小时的HCG比值、孕酮水平（0小时和48小时）以及PUL的临床结局。我们利用该数据集和专家信息开发贝叶斯网络以预测异位妊娠。
最佳贝叶斯网络使用了孕周、HCG比值和48小时的孕酮水平，在前瞻性测试时预测异位妊娠的受试者操作特征曲线下面积（AUC）为0.88。
离散值贝叶斯网络比例如逻辑回归更复杂。然而，我们已经证明这样的模型可用于预测PUL人群中的异位妊娠。需要进行前瞻性干预多中心研究以验证此类模型在临床实践中的应用。
