%0 Journal Article
%T Early Magnetic Resonance Imaging Measurements and Prediction of Second Trimester Pregnancy Loss: a Nomogram Model Analysis.
%A Liu J
%A Xu M
%A Zhou L
%A Yang L
%A Li H
%A Li X
%J Int J Womens Health
%V 16
%N 0
%D 2024
%M 38765204
%F 2.59
%R 10.2147/IJWH.S453867
%X UNASSIGNED: To investigate the magnetic resonance imaging (MRI) features of women with prior second-trimester pregnancy loss, and to establish a nomogram prediction model for subsequent miscarriage.
UNASSIGNED: A retrospective cohort study of women with prior second-trimester pregnancy loss from January 2018 to December 2021 in Second Affiliated Hospital of Soochow University was performed. A total of 245 patients were included. Data from January 2018 to December 2019 were used to construct the model, and data from January 2020 to December 2021 were used to evaluate the model. Data on maternal demographic characteristics, MRI cervical measurements were extracted. The prediction model was constructed with independent variables determined by multivariate logistic regression analyses. Through receiver-operating characteristic (ROC) curve analysis, the predictive ability of the model for subsequent second trimester pregnancy loss in women was evaluated, and internal validation was performed through validation data.
UNASSIGNED: Thin cervix was observed in 77 (31.42%) women with prior second-trimester pregnancy loss, the mean longitudinal diameter of cervical canal on MRI was 11.76±2.75mm. The model reached a sensitivity of 80%, specificity of 75.90%, positive predictive value (PPV) of 55.80% and negative predictive value of 90.90%; ROC characteristics proved that the model was superior to any single parameter with an AUC of 0.826.
UNASSIGNED: Our observations showed that thin cervix and longitudinal diameter of cervical canal reliably predicted second trimester pregnancy loss. We developed and validated a nomogram model to predict the individual probability of second trimester pregnancy loss in the next pregnancy and hopefully improve the prediction and indication of interventions.