Selection of high-risk individuals for a large niche development based
on a scoring classification model: a retrospective cohort study.
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.