Early prediction of placenta accreta spectrum by evaluation of risk factors and ultrasound

Introduction We explored the predictive utility of clinical risk factors and first-trimester pregnancy ultrasound signs for severe placenta accreta spectrum. Material and methods Patients with placenta accreta spectrum treated in our institution between March 1, 2017 and December 31, 2022 were analyzed. The patients were divided into those with mild and those with severe conditions. Univariate analysis was used to determine the clinical risk factors and first trimester ultrasound signs associated with severe placenta accreta spectrum. Receiver operating characteristic curves were drawn and the areas under the curves calculated. Results Univariate analysis revealed significant between-group differences between the groups in the number of cesarean sections, scar pregnancy, low gestational sac position, abnormal placental position, focal exophytic mass and abnormal placental lacunae in placental parenchyma (p < 0.05). The number of cesarean sections and first trimester ultrasound signs predicted severe placenta accreta spectrum, with areas under the curves of 0.66 and 0.75. When the number of cesarean sections was combined with low gestational sac position, the placenta position and related ultrasound signs, the areas under the curves for predicting severe placenta accreta spectrum were 0.78, 0.73, and 0.89, respectively. Conclusions Clinical risk factors and first-trimester ultrasound signs predicted severe placental accreta spectrum and their combinations had even greater clinical utility.

Authors

Institutions

Publication Details

Journal
Archives of Medical Science
Published
2024-10-31
DOI
https://doi.org/10.5114/aoms/176673
Primary Topic
Maternal and fetal healthcare
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Early prediction of placenta accreta spectrum by evaluation of risk factors and ultrasound

Weishe Zhang, Yuelan Liu, Jingrui Huang, Caihong Hu et al.
Archives of Medical Science
Maternal and fetal healthcare
article

Early prediction of placenta accreta spectrum by evaluation of risk factors and ultrasound

Weishe Zhang, Yuelan Liu, Jingrui Huang, Caihong Hu, Yiran Fu, Qi Li, Biaobiao Wang, Chenlin Pei, Qiaozhen Peng
article en

Abstract

Introduction We explored the predictive utility of clinical risk factors and first-trimester pregnancy ultrasound signs for severe placenta accreta spectrum. Material and methods Patients with placenta accreta spectrum treated in our institution between March 1, 2017 and December 31, 2022 were analyzed. The patients were divided into those with mild and those with severe conditions. Univariate analysis was used to determine the clinical risk factors and first trimester ultrasound signs associated with severe placenta accreta spectrum. Receiver operating characteristic curves were drawn and the areas under the curves calculated. Results Univariate analysis revealed significant between-group differences between the groups in the number of cesarean sections, scar pregnancy, low gestational sac position, abnormal placental position, focal exophytic mass and abnormal placental lacunae in placental parenchyma (p < 0.05). The number of cesarean sections and first trimester ultrasound signs predicted severe placenta accreta spectrum, with areas under the curves of 0.66 and 0.75. When the number of cesarean sections was combined with low gestational sac position, the placenta position and related ultrasound signs, the areas under the curves for predicting severe placenta accreta spectrum were 0.78, 0.73, and 0.89, respectively. Conclusions Clinical risk factors and first-trimester ultrasound signs predicted severe placental accreta spectrum and their combinations had even greater clinical utility.

Archives of Medical Science
Central South University (CN), Hunan Provincial Center for Disease Control and Prevention (CN), Xiangya Hospital Central South University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation
Good health and well-being
Openalex Percentile: Top 69%
Maternal and fetal healthcare
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.