Introduction
Ectopic pregnancy (EP) occurs when
fertilized ovum locate outside the uterine cavity, and its incidence
rate is about 1.5% to 2% in pregnancy1. The death
toll of ectopic pregnancy can account for more than 6% of maternal
mortality, which is the leading cause of maternal death in early
pregnancy2, 3. According to a research in
Scotland4, nearly half of the patients had to visit
the clinicians more than three times to be diagnosed for ectopic
pregnancy. Moreover, it is estimated that 136, 400 pounds sterling has
been spend yearly on the diagnosis of EP in pregnancy of unknown
location(PUL). PUL refers to pregnancy test positive, but the first
transvaginal ultrasound (TVS) has not found evidence of pregnancy in
both intrauterine and extrauterine, accounting for about 5% to 42% of
all early pregnant women5. Among them, ectopic
pregnancy, accounted for 6% to 20% of all PUL patients,required
surgery or chemotherapy and had relatively higher mortality comparing
with the rest of PUL patients.
The elevated serum hCG and progesterone are the most widely used
indications for early pregnancy. Some protocols use a single biomarker
to distinguish patients with different
outcomes by setting certain
threshold, while others use established mathematical formulas. In
European countries and regions, the logistic regression model M4,
established by George Condous et al6, is now the most
widely used formula for predicting the outcomes of PUL. But the
sensitivity for EP was too low for the model to be used in clinical
practice in the US7. This suggests that different
definitions of pathology and disease management processes may lead to
distinct accuracy of prediction protocols in specific regions. Thus,
there is no international consensus on how to manage women with a
pregnancy of PUL. In this study, we will plot the SROC curve and
calculate the area under the
curve(AUC)by meta-analysis of studies, jointly contrasting the
sensitivity and specificity of individual protocols. We will introduce
the concepts of average production utility and marginal benefit
assessing the feasibility of individual protocols, and attempt to
describe the impact of the complexity of the protocols.