Competing-risks model for prediction of small-for-gestational-age neonates at 36 weeks' gestation.

To develop further a new competing risks model for the prediction of small-for-gestational-age (SGA) neonates, by including sonographically estimated fetal weight (EFW) and biomarkers of impaired placentation at 36 weeks' gestation. To compare the new model with the traditional EFW <10 percentile cut-off. This is a prospective observational study in 29,035 women with singleton pregnancies undergoing routine ultrasound examination at 35+0 to 36+6 weeks' gestation. We used the competing risks model for prediction of SGA. The parameters for the prior history model were those presented in previous studies. An interaction continuous model was used for the EFW likelihood. A folded plane regression model was fitted to describe likelihoods of biomarkers of impaired placentation. Stratification plans were also developed. The new model was evaluated and compared with EFW percentile cut-offs. The prediction of SGA was better for gestational intervals closer to the point of assessment. The prediction provided by maternal factors alone was improved significantly by addition of EFW, uterine artery pulsatility index (UtA-PI) and placental growth factor (PlGF), but not mean arterial pressure or soluble fms-like tyrosine kinase-1. At 10% false positive rate, maternal factors and EFW predicted 77.6% and 65.8% of SGA <10 percentile delivered before 38 and 42 weeks, respectively. The respective figures for SGA <3 percentile were 85.5% and 74.2%. Addition of UtA-PI and PlGF resulted in marginal improvement in prediction of SGA <3 percentile. A competing risks approach that combines maternal factors and EFW is better than fixed EFW percentile cut-offs, in predicting SGA, especially with increasing time interval between assessment and delivery. The new model was well calibrated. A competing risks model provides effective risk stratification for SGA neonates at 35+0 to 36+6 weeks' gestation, which is superior to EFW percentile cut-offs. Addition of biomarkers of impaired placentation results in small improvement of the predictive performance for SGA neonates with severe smallness achieved by maternal factors and fetal biometry alone. This article is protected by copyright. All rights reserved. [Abstract copyright: This article is protected by copyright. All rights reserved.]