Abstract
BACKGROUND: Preeclampsia (PE) is a major cause of adverse maternal and infant outcomes. Accurate screening of PE is currently the focus of clinical attention. This study aimed to develop a model for predicting PE. METHODS: A retrospective case-control study was conducted with 916 pregnant women who received care at the Second Hospital of Tianjin Medical University (October 2018 to July 2020). Women were randomly divided into the training (n=680) and testing (n=236) sets based on a ratio of 3:1. Demographic and clinical data of women were collected. In training set, logistic regression (LR), classification tree (CT) model, and random forest (RF) algorithm were used to develop prediction models for PE. Using the testing set was to validate these prediction models. The predictive performance of three models were assessed by the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: Of the total 916 women, 237 had PE. The family history of hypertension, pre-pregnancy body mass index (pBMI), blood pressure (BP) ≥130/80 mmHg in early pregnancy, age, chronic hypertension, and duration of hypertension were the predictors of PE. The AUCs for the LR, CT, and RF models were 0.778, 0.850, and 0.871, respectively (all P<0.05 for all pair-wise comparisons). The RF had the best predictive efficiency with sensitivity, specificity, PPV, and NPV of 79.6%, 94.7%, 79.6%, and 94.7%, respectively. CONCLUSIONS: The RF model could be a practical screening approach for predicting PE, which is helpful for clinicians to identify high-risk individuals and prevent the occurrence of adverse pregnancy outcomes.