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.