Development and validation of a preeclampsia prediction model for the first and second trimester pregnancy based on medical history

作者：Qi Xu, Lili Xing, Ting Zhang, Guoli Liu · 发表于：BMC Pregnancy and Childbirth · 年份：2025 · DOI：10.1186/s12884-025-07733-7 · 被引用次数：2 · 研究领域：Pregnancy and preeclampsia studies、Maternal and fetal healthcare、Gestational Diabetes Research and Management

OBJECTIVE: The study aimed to identify the risk factors of preeclampsia (PE) and establish a novel prediction model. STUDY DESIGN: A retrospective, single-center analysis was conducted using clinical data from 5099 pregnant women who gave birth at Peking University People's Hospital between June 2015 and December 2020 who had placental growth factor (PIGF) levels records at 13-20 + 6 gestation weeks. The participants were randomly divided into a training set (70%, n = 3569) and a validation set (30%, n = 1030), between which the consistency was checked, and the analysis was performed according to whether PE occurred during pregnancy. Factors with univariate logistic analysis outcome of p < 0.2 were incorporated into the multivariate logistic regression analysis model, then variable selection by stepwise regression with AIC as the criterion was executed to finally identify the variables used for modeling. The model's discriminative ability was assessed using the receiver operating characteristic (ROC) curve, and its calibration was evaluated through calibration curves and Hosmer-Lemesow test. In addition, decision curve analysis (DCA) was used for clinical net benefit appraisal. RESULTS: Logistic regression analysis identified nine risk factors for PE, including: maternal age (OR = 1.072, 95%CI = 1.025-1.120), parity(OR = 0.718,95%CI = 0.470-1.060), pre-pregnancy BMI (OR = 2.842,95%CI = 1.957-4.106), family hypertension history (OR = 3.604,95%CI = 2.433-5.264), pregestational ...