Construction of a Prediction Model for Adverse Perinatal Outcomes in Foetal Growth Restriction Based on a Machine Learning Algorithm: A Retrospective Study

作者：Xiangli Meng, Lei Wang, Minghui Wu, Na Zhang, Xiaofei Li, Qingqing Wu · 发表于：BJOG An International Journal of Obstetrics & Gynaecology · 年份：2025 · DOI：10.1111/1471-0528.18226 · 被引用次数：5 · 研究领域：Pregnancy and preeclampsia studies、Gestational Diabetes Research and Management、Cancer Risks and Factors

OBJECTIVE: To create and validate a machine learning (ML)-based model for predicting the adverse perinatal outcome (APO) in foetal growth restriction (FGR) at diagnosis. DESIGN: A retrospective study. SETTING: Multi-centre in China. POPULATION: Pregnancies affected by FGR. METHODS: We enrolled singleton foetuses with a perinatal diagnosis of FGR who were admitted between January 2021 and November 2023. A total of 361 pregnancies from Beijing Obstetrics and Gynecology Hospital were used as the training set and the internal test set. In comparison, data from 50 pregnancies from Haidian Maternal and Child Health Hospital were used as the external test set. Feature screening was performed using the random forest (RF), the Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression (LR). Subsequently, six ML methods, including Stacking, were used to construct models to predict the APO of FGR. MAIN OUTCOME MEASURES: Model's performance was evaluated through indicators such as the area under the receiver operating characteristic curve (AUROC). The Shapley Additive Explanation analysis was used to rank each model feature and explain the final model. RESULTS: Mean ± SD gestational age at diagnosis was 32.3 ± 4.8 weeks in the absent APO group and 27.3 ± 3.7 in the present APO group. Women enrolled in the present APO group had a higher rate of hypertension related to pregnancy (74.8% vs. 18.8%, p < 0.001). Among 17 candidate predictors (including maternal characteris...