Main findings
Laparoscopy was the gold standard
for the diagnosis of ectopic pregnancy for a long period of time since
199338. However, it is reported that 3.0%
~ 4.5% of EP patients failed to diagnose EP at the time
of initial laparoscopic examination39, 40. As a
result, laparoscopy is no longer considered as the gold standard for the
diagnosis of ectopic pregnancy according to the 2016 Royal College of
Obstetricians and Gynaecologists and Association of Early Pregnancy
Units (RCOG/AEPU) joint guidelines on diagnosis and management of
ectopic pregnancy41 . Currently, there is a lack of
effective means for early diagnosis of ectopic pregnancy, because it was
difficult to distinguish from threatened or inevitable abortion,
intrauterine pregnancy with corpus luteum rupture and intrauterine and
extrauterine pregnancy. The American College of Obstetricians and
Gynaecologists (ACOG) and RCOG/AEPU joint guidelines recommend that
transvaginal ultrasound is the first choice for the diagnosis of
EP41, 42. However, ultrasound examination is also
affected by equipment, pathway, physician’s operation, pregnant women’s
obesity, combined with uterine fibroids or ovarian tumors. 8% to 31%
of the early pregnant site cannot be diagnosed at the first ultrasound
examination19.
PUL was a temporary status with various outcomes. EP is the high risk
outcome of PUL, because it would cause internal hemorrhage and endanger
patients’ lives1. The high-risk outcomes as EP in PUL
patients need to be screened out before the occurrence of rupture and
internal hemorrhage43. Precise prediction of PUL
outcome can provide not only timely and correct management protocols for
EP, but also reduce medical burden and unnecessary medical intervention
for IUP and FPUL patients.
The M4 model is currently one of the most widely used prediction models,
especially in European countries like Britain.
In 2016, Ben Van Calster et
al26 proposed the M6 model, which introduced both
progesterone and hCG values(0h and 48h)as variables in anticipation of
greater predictive accuracy, because the presence of low serum
progesterone concentrations in patients with EP has been known since the
late 1970s44. A meta-analysis by S Bobdiwala et al. in
2019 showed that the areas under the curves (95% CI) of hCG cut-offs,
hCG ratio (0/48h), progesterone cut-offs and M4 model were 0.42
(0.00-0.99), 0.69 (0.57-0.78), 0.69 (0.54-0.81) and 0.87 (0.83-0.91),
respectively. The prediction accuracy of the model with hCG at 0 h and
48 h was higher than that with single hCG value, and the model with
single progesterone value had higher accuracy than that with single HCG
value, which is the same trend as our
statistics. Moreover, through our
systematic meta-analysis of all published prediction protocols of
ectopic pregnancy outcome in PUL, we found that, consistent with
previous studies45, M4 model has better prediction
accuracy. As for the latest protocol, M6 also showed a trend of higher
prediction accuracy, reaching the AUC of 0.94. It is worth noting that
hCG ratio and progesterone cut-offs also showed good sensitivity and
specificity in the scheme using a single biomarker or single biomarker
detection point, with AUC of 0.82 and 0.72, respectively. In view of the
short time required, few testing items, fast guidance for clinicians
(especially in developing countries and regions), and reduced cost for
patients, they still have certain practical value. Most studies focusing
on PUL outcome prediction models have been conducted in European and
North American countries and regions with abundant medical resources.
There often were well-established protocols for early pregnancy
diagnosis, but fewer studies had been conducted on PUL outcome
prediction in low-income countries and areas. In addition, predicting EP
in PUL patients by M4 in the United Kingdom and United States, Barnhart
K. T. et al.7 found that even after adjusting the
diagnostic criteria to a consistent level in the two countries, the
sensitivity of the model differed. The study revealed that the PUL
outcome prediction protocols were related to the database used, or the
different medical guidelines and the levels of healthcare organization.
In the past, the acceptability of each prediction protocol has hardly
been evaluated, so we propose to use average production utility to
evaluate the cost performance of the protocols. Due to the differences
of medical charges, medical insurance policies and health policies in
different countries and regions, and the differences of medical
development level, we use the sum of
the number of visits and the number of inspection items to replace the
medical cost, and the number of inspection items is defined as the
minimum number of inspections that can be used to predict the outcome of
the protocols. It is reasonable to assume the numbers of tests the
protocols required can reflect the medical cost. In addition, more
visits and examination items, and more complex prediction protocols
could likely lead to follow-up losing. Therefore, we believe that the
data loss caused by the above reasons reflects the acceptability of each
protocol to a certain extent. We propose a new evaluation method. Table
2 shows the relationship between the sum of the number of visits and the
number of inspection items and the rate of lost. Previously, we expected
that as the protocol took longer and the number of examinations and
visits increased, more patients might not be able to use the protocols
because of medical related payment pressure and severe clinical
symptoms, which may lead to the loss of follow-up and the lack of timely
diagnosis and treatment of EP patients. However, different from our
expectation, the rate of lost did not increase with the number of
examinations and visits required by the protocols. When the time
required for the protocols was extended from one day to two days, the
average rate of lost increased from 11.56% (95% CI 6.96% - 16.16%)
to 17.46% (95% CI 11.46% - 23.46%). Although there was a certain
growth trend, there was no statistical significance. This may be due to
the following reasons: First, the earliest time of all 29 studies can be
traced back to 1991, and the latest time is 2018. Over the past 20
years, great changes may have taken place in medical policy,
popularization of medical science knowledge and national economic level,
which may affect the willingness of patients to follow up and the
ability of medical institutions to track and manage patients; Second,
some protocols (such as P1, M1, etc.) have not been studied extensively,
which may cause large bias. In fact, after excluding the protocols with
less than or equal to 2 studies, the average rate of lost increased from
11.19% (95% CI 4.67-17.72) to 18.63% (95% CI 9.67-17.71) when the
sum of visits and examinations changed from 3 to 5. Although there was
no statistical significance, the trend was the same as our prediction.
Besides that, table 3 shows that the complicated protocols to improve
the accuracy of prediction also needs higher cost. The average
production utility of M4 model, which requires at least 2 visits and 3
examinations, is higher than that of M6 model requiring at least 3
visits and 3 examinations, and lower than the hCG cutoffs model and
progesterone-cutoffs model which only need one visit and one inspection.
When this trend is reflected in clinical work, it seems that more
complex prediction schemes may bring higher costs. Simple prediction
protocols still have certain application value in low-income countries
and regions.