Abstract
Objective: To develop a risk prediction model to identify the
high-risk individuals of large niche formation after cesarean section
(CS).
Design A retrospective study.
Setting Women’s health research in Anhui, China.
Population: Women received CS between Jan 2012 to Jun 2017.
Methods: Women were arranged to receive uterine scar
examination by transvaginal ultrasonography, and those diagnosed with
niche were divided into two groups according to whether they suffer from
postmenstrual spotting. The cut-off values of depth, RMT (residual
myometrium thickness), and depth/AMT (adjacent myometrium thickness)
were chosen to define a large niche. Then, all participants were
classified into three groups, including a control, a small niche, and a
large niche group. The scores of each variable in the prediction model
were calculated by dividing the minimum β-coefficient from the
multivariate logistic analysis.
Main outcome: Primary outcome was a prediction scoring model
for large niche formation.
Results: In total, 727 women were recruited in this study, and
the large niche was defined as more than 0.50 cm in depth, less than
0.21 cm in RMT, more than 0.56 in depth/AMT. The large niche prediction
model included eight variables of age at delivery, retroflexed uterine,
meconium-stained amniotic fluid, history of CS, B-Lynch suture,
operation duration, premature rupture of membranes and cervical
dilatation more than 4 cm. The cut-off value of 5 in this score-based
model presented sensitivity and specificity as 67.48% and 90.07%
respectively.
Conclusions: This score-based risk prediction model could
present the risk of large niche formation of individuals after CS.
Tweetable abstract: A score-based large niche prediction system
is an effective tool to prevent large niche formation.
Funding: National Key
Research and Development Program. Grant Number: 2016YFC1000204
The funding sources had no involvement in the study design; in the
collection, analysis and interpretation of data; in the writing of the
report; or in the decision to submit the article for publication.
Key words: Cesarean, large niche, definition, risk prediction,
scoring, an individualized assessment
Introduction
Cesarean section (CS) is one of
the most common operations performed on reproductive-aged
women1,2. In China, nearly 35% of the deliveries are
by CS3. Long term complications of CS, including
postmenstrual spotting4, scar
pregnancy1,5, and scar dehiscence or rupture in later
pregnancies6, are usually caused by incomplete healing
of the previous cesarean incision. It forms a triangular anechoic
structure at the site of the uterus scar with depth more than 2 mm and
is defined as a niche1. The prevalence of the niche
varies between 24% and 70% in random populations by transvaginal
ultrasonography (TVS)1,7,8.
Small niches may indeed be quite
common but would be clinically unimportant, however, the large niches
are most likely to give rise to those complications and should be taken
seriously9,10. So far, only three small sample studies
have defined large niche as a depth of at least 50-80% of anterior
myometrium or the remaining
myometrial thickness (RMT) less than 2.2 mm when evaluated by
TVS11-13. Large niche incidence has been reported
varying from 11% to 45% depending on the definition mentioned
above6. A recent study indicated that the size of the
niche can guide clinical decision and the sum of depth and length
greater than 40 mm can increase the failure risk of surgical
outcomes9. However,
research data on large niche
development based on large sample sizes is lack.
Large niche is pathological14,
the selection of individuals at
high-risk based on scoring classification model could be efficient for
developing preventive strategies. Several studies have summarized
numerous risk factors related to niche
formation5,15,16, and emphasized that a double layer
closure of the cesarean incision was advantageous to decrease the
incidence of niche and increase the RMT17. As we know,
cervical dilation, CS history, and uterus position have been commonly
accepted as niche risk factors in the previous
studies16,18,19, however, there is debate on the other
factors, such as twin pregnancy, obesity, surgeon
experience6,16 owing to small sample size in different
studies.
Hence, this study aims to define the large niche based on large sample
size statistical analysis and develop a score-based prediction model to
identify an individual’s risk of developing a large niche after CS.
Materials and Methods
Study population
This study was approved by the Ethics Committee of the First Affiliated
Hospital of Anhui Medical University before the study began. All
participants provided informed consent before entering the study. This
study has lasted more than 4 years from Jan. 2016 to Jun. 2020. Randomly
drawing 1641 non-pregnant women who have received CS more than one
year20 in our department between Jan 2012 to Jun 2017
were asked to complete questionnaires: menstruation cycle, methods of
contraceptive, dysmenorrhea, abnormal uterine bleeding, infertility,
dyspareunia, gynecological endocrine disease, whether have another baby
or have suffered other surgeries. Finally, 812 women accepted the
invitation and were arranged to receive TVS examination. Excluding
criteria were other surgeries on the uterus, intrauterine device (IUD),
oral contraceptive, irregular vaginal bleeding related to the endocrine
disorder, and endometrium polyp or carcinoma. Postmenstrual spotting was
defined as more than 2 days of brownish discharge at the end of
menstruation with a total length of menstruation (including spotting) of
more than 7 days, or intermenstrual bleeding which starts within 5 days
after the end of menstruation21. The flow chart was
shown in Fig.S1.
Cesarean scars measurement
All the participants were arranged during their mid-follicular phase to
receive TVS examination (Fig. 1A, B). The standardized approach for
measuring CS scars was from midsagittal plane to both sides of the
uterus with good visualization of the cervical canal, recording the
information about uterus position, endometrium thickness, RMT,
adjacent myometrial thickness
(AMT) of the scar, depth, length for the niche22,23(as shown in Fig. 1C., and Table 1). The width of the niche should be
visualized in the transverse plane. All values were taken as the average
of three times of examination. The uterine cavity was examined carefully
for the presence of other intrauterine abnormalities, such as
Naeschner’s cyst of the cervix, submucosal fibroids or polyps. Niche was
defined as an indentation at the site of the cesarean scar with a depth
of at least 2 mm in the sagittal plane8.
Data collection and selection of candidate predictors of a large niche
In the present study, candidate variables predictive of the large niche
were identified based on the current literature, meta-analyses,
high-quality studies1,15. Thirty-one variables were
divided into four parts as shown in Table 2, including Operation
(history of once CS, bilateral tubal ligation (i.e. for sterilization),
B-Lynch suture, emergent CS, duration of CS and surgeon experience),
Infection (meconium-stained
amniotic fluid (MSAF), cervical dilatation,
premature rupture of membranes
(PROM), and vaginal examination), Tension (pre-pregnancy
bass mass index (BMI), BMI at
delivery, retroflexed uterine, macrosomia, twin pregnancy, breech,
presence or duration of labor at CS, and oxytocin augmentation during
labor), and Healing (pre-eclampsia, diabetes,
intrahepatic cholestasis of
pregnancy (ICP), anemia (Hb < 90 g/mL), postpartum hemorrhage,
placenta previa, steroid treatment during pregnancy and assisted
reproduction technology (ART)). In this study, all the participants
received a continuous locked single-layer uterine suture with peritoneal
closure and inclusion of the decidua.
Statistical analysis
Analysis of the niche parameters
Women with niche were grouped into two parts according to whether they
complained of obvious postmenstrual spotting symptoms. The average
menstruation days (including dot bleeding days) and parameters of the
niche (depth, length, width, RMT, AMT, and depth/AMT) were compared
between the asymptomatic group and symptomatic group usingt-test , and p < 0.05 was regarded as
significantly different (Table 1). As shown in Table
S2 and Fig 2, we used
receiver-operating characteristics (ROC) curves to establish cut-off
values for depth, RMT, depth/AMT, width, length, and AMT for
classification of large niche.
Univariate analyses and multivariate logistic regression analyses of
risk factors of a large niche
In this study, women with niche but the parameters of the niche didn’t
meet our definition of the large niche were divided into small niche
group. Therefore, all the participants were classified into three groups
including, the control group, small niche group, and large niche group
as shown in Table S3. Univariate analyses between different groups were
conducted using the t-test for continuous variables, and thechi-squared test for categorical variables. Two-sided tests were
used and p < 0.05 was considered statistically
significant. Then, variables with p < 0.05 in univariate
analyses were included in the multiple logistic regression analyses. The
odds ratio (OR) and 95% CI for the association of a large niche
development with candidate predictors were estimated using multivariate
logistic regression, and p < 0.05 was deemed to be
statistically significant (Table S4).
Score-based model for prediction of the risk of a large niche
The risk scores of each predictor in the model were calculated by
dividing the minimum β -coefficient from the multivariate logistic
analyses and rounding to the nearest 0.5. The total risk score of each
participant was calculated by summing the scores of each risk factor,
and then, a score-based prediction model was developed. The cut off
value of the high risk of large niche development was assessed by the
area under the ROC curve (AUC) and its 95% CI.
All statistical analyses were performed using the computer package SPSS
version 23.0 (IBM Corp., Armonk, New York). The data were presented as
mean ± SD for normally distributed variables and frequency (percentage,
%) for categorical variables.