loading page

Selection of high-risk individuals for a large niche development based on a scoring classification model: a retrospective cohort study.
  • +6
  • Wang Jing,
  • Qiushi Pang,
  • Wenwen Wei,
  • Fen Huang,
  • Yunxia Cao,
  • Mingjun Hu,
  • Shijie Yan,
  • Linghui Cheng,
  • Zhaolian Wei
Qiushi Pang
Author Profile
Wenwen Wei
Author Profile
Yunxia Cao
Author Profile
Mingjun Hu
Author Profile
Shijie Yan
Author Profile
Linghui Cheng
Author Profile
Zhaolian Wei
Author Profile

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.