Establishment of a risk prediction model for postpartum hemorrhage caused by pernicious placenta previa in pregnant women using uterine arcuate artery ultrasound parameters based on logistic regression
HUANG Qiaoru
LI Zilin
LIU Yingfeng
YIN Wanping
CHEN Jinghua
HUANG Lishan
ZHOU Qianjun
Abstract：Objective To examine the pregnant women with pernicious placenta previa(PPP) using uterine arcuate artery ultrasound,and to establish a risk prediction model for postpartum hemorrhage using ultrasound parameters based on logistic regression.Methods A total of 103 PPP pregnant women who gave birth in the Tenth Affiliated Hospital of Southern Medical University(Dongguan People's Hospital) from June 2019 to June 2022 were selected and their postpartum hemorrhage was followed up,and the PPP pregnant women were divided into hemorrhage group( The amount of blood lost within the first 24 hours after delivery≥1000 mL was difined as the occurrence of postpartum hemorrhage) and non-hemorrhage group( The amount of blood lost within the first 24 hours after delivery＜0.05).The uterine arcuate artery ultrasound parameters of PSV,RI and PI were the influencing factors of postpartum hemorrhage(P＜0.05).A logistic regression model was established:logit(P)=-40.002+1.386 × PSV-0.926 × RI-0.783 × PI,with good fitting degree.The area under the curve( AUC) for predicting postpartum hemorrhage was 0.860 based on the fitted model,which was larger than that for predicting postpartum hemorrhage by using single indicator of PSV(x2=2.355,P=0.019),RI(x2=2.332,P=0.020) and PI(x2=2.472,P=0.013).Conclusion The risk prediction model established for postpartum hemorrhage caused by PPP in pregnant women using uterine arcuate artery ultrasound parameters based on logistic regression can effectively determine the risk of postpartum hemorrhage,and the risk prediction model is helpful for early prevention and treatment in clinical practice.
Keywords：Logistic regressionUterine arcuate artery ultrasound parametersPernicious placenta previa(PPP)Postpartum hemorrhageRisk prediction model
Publication Date：2024-11-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1267-1271 )