Identification of risk factors and development of a predictive model for foetal growth restriction: a retrospective case–control study

Objective To identify risk factors associated with foetal growth restriction (FGR) and develop a predictive model to support early diagnosis and individualized clinical management strategies.Methods A retrospective case-control study was conducted at Wusibei Campus of Fujian Maternity and Child Health Hospital, Fuzhou, China involving 490 pregnant women (196 FGR cases and 294 controls) who delivered between January and December 2024. Participants were categorized into two groups: a control group with normal foetal growth and an FGR group with confirmed FGR (This retrospective case–control study selected participants according to pregnancy outcomes and analysed clinical data retrospectively). Clinical characteristics and maternal-neonatal outcomes were compared. Univariate and multivariate logistic regression analyses were performed using SPSS 26.0 to determine independent risk factors for FGR. A predictive nomogram was constructed using R software (version 4.1.0) with the rms package. Model performance was evaluated via bootstrap internal validation, and discrimination and calibration were assessed using receiver operating characteristic (ROC) curve analysis and calibration plots.Results The FGR group demonstrated significantly higher incidences of foetal distress, caesarean delivery, preterm birth, and neonatal hospitalization compared to the control group (all p < 0.001). Multivariate analysis identified the following independent risk factors for FGR: reduced placental thickness on first-trimester nuchal translucency ultrasound, low pregnancy-associated plasma protein A levels, hypertensive disorders of pregnancy, gestational hypothyroidism, oligohydramnios, and absence of gestational diabetes mellitus. The resulting nomogram demonstrated moderate predictive capability with an area under the ROC curve of 0.722.Conclusion The predictive model developed in this study may facilitate the early identification of high-risk pregnancies, enabling timely intervention to improve maternal and neonatal outcomes. However, external validation of this model is needed before clinical implementation.