International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2025
Development of a time-dependent random survival forest model for predicting FGR based on prenatal screening date.
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Ruan Yan, Zhang Pan-Xi, Zhu Yi-Yang
Аннотация
OBJECTIVE: Fetal growth restriction (FGR) is a progressive condition that amplifies risks with advancing gestation. This study develops a time-dependent random survival forest (RSF) model integrating prenatal screening markers to predict FGR and explore temporal factors, aiding clinical decision making for targeted intervention.
METHODS: This was a retrospective cohort study in tertiary hospitals in Taizhou City, China (2016-2022) including 27 543 singleton pregnancies with serial prenatal records, excluding major fetal anomalies or incomplete follow-up. An RSF model was developed using gestational week as the time variable and Delphi-defined FGR as the event. The model incorporates maternal biological data, serologic markers, and ultrasound measurements as feature variables. Performance was assessed using time-dependent receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Feature-based cumulative risk curves were employed for interpreting the model. Main outcomes were measured using model validation (C-index, area under the curve [AUC]) and utility (individual prediction curves, feature risk curve).
RESULTS: The RSF model demonstrated high performance (C-index: 0.864), with peak predictive power between 28 and 36 weeks' gestation (AUC: 0.87-0.91). It identified both early- and late-onset FGR. Key predictors included ultrasound markers such as abdominal circumference and femur length. Late-onset FGR was mainly caused by a cumulative mild effect of multiple factors, while early-onset FGR was driven by maternal factors and fetal markers like AFP and E3.
CONCLUSION: The RSF model outperforms traditional models in accuracy and flexibility, providing dynamic FGR risk predictions and supporting timely clinical intervention.
METHODS: This was a retrospective cohort study in tertiary hospitals in Taizhou City, China (2016-2022) including 27 543 singleton pregnancies with serial prenatal records, excluding major fetal anomalies or incomplete follow-up. An RSF model was developed using gestational week as the time variable and Delphi-defined FGR as the event. The model incorporates maternal biological data, serologic markers, and ultrasound measurements as feature variables. Performance was assessed using time-dependent receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Feature-based cumulative risk curves were employed for interpreting the model. Main outcomes were measured using model validation (C-index, area under the curve [AUC]) and utility (individual prediction curves, feature risk curve).
RESULTS: The RSF model demonstrated high performance (C-index: 0.864), with peak predictive power between 28 and 36 weeks' gestation (AUC: 0.87-0.91). It identified both early- and late-onset FGR. Key predictors included ultrasound markers such as abdominal circumference and femur length. Late-onset FGR was mainly caused by a cumulative mild effect of multiple factors, while early-onset FGR was driven by maternal factors and fetal markers like AFP and E3.
CONCLUSION: The RSF model outperforms traditional models in accuracy and flexibility, providing dynamic FGR risk predictions and supporting timely clinical intervention.