ArticleFrontiers in medicine2026
Twin growth discordance trajectories and fetal growth velocity for predicting adverse neonatal outcomes: a multicenter cohort study.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Twin pregnancies are associated with a substantially increased risk of adverse neonatal outcomes. However, it remains unclear whether longitudinal changes in intertwin growth difference and fetal growth rate add prognostic information. Objective: To characterize longitudinal patterns of intertwin growth discordance and evaluate whether combining these patterns with fetal growth velocity (FGV) improves prediction of adverse neonatal outcomes in twin pregnancies. Methods: This multicenter retrospective cohort study included 1,930 twin pregnancies with at least three ultrasound examinations, with one center used for model development and another for external validation. Group-based trajectory modeling (GBTM) was applied to identify longitudinal discordance patterns. FGV was calculated by linear regression of serial estimated fetal weight. Multivariable logistic regression models were constructed to evaluate predictive performance, with exploratory machine learning analysis performed to assess potential non-linear effects. Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC). Results: Four intertwin growth discordance trajectories were identified: Stable low (84.1%), Low rapid increasing (8.5%), High decreasing (5.1%), and High increasing (2.3%). Compared with the Stable low trajectory, both High decreasing (adjusted OR [aOR] 3.24; 95% CI 1.87-5.55) and High increasing (aOR 2.99; 95% CI 1.44-6.22) trajectories were independently associated with adverse neonatal outcomes. Addition of smaller twin FGV significantly improved discrimination beyond trajectory classification alone, and the combined model demonstrated moderate performance (development AUC 0.696; validation AUC 0.733). Exploratory machine learning analysis showed comparable performance (external validation AUC 0.710), and SHAP analysis identified smaller twin FGV as the most influential predictor. Conclusion: Integration of longitudinal discordance trajectories with fetal growth velocity may provide clinically relevant prognostic information beyond static measurements and may support individualized prenatal surveillance in twin pregnancies.
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