Prediction of adverse neonatal adaptation in fetuses with severe fetal growth restriction after 34 weeks of gestation

Abstract

Objective: To establish a predictive model for adverse immediate neonatal adaptation (INA) in fetuses with suspected severe fetal growth restriction (FGR) after 34 gestational weeks (GW).

Methods: We conducted a retrospective observational study at the University Hospitals of Strasbourg between 2000 and 2020, including 1,220 women with a singleton pregnancy and suspicion of severe FGR who delivered from 34 GW. The primary outcome (composite) was INA defined as Apgar 5-minute score <7, arterial pH <7.10, immediate transfer to pediatrics, or the need for resuscitation at birth. We developed and tested a logistic regression predictive model.

Results: Adverse INA occurred in 316 deliveries. The model included six features available before labor: parity, gestational age, diabetes, middle cerebral artery Doppler, cerebral-placental inversion, onset of labor. The model could predict individual risk of adverse INA with confidence interval at 95 %. Taking an optimal cutoff threshold of 32 %, performances were: sensitivity 66 %; specificity 83 %; positive and negative predictive values 60 % and 87 % respectively, and area under the curve 78 %.

Discussion: The predictive model showed good performances and a proof of concept that INA could be predicted with pre-labor characteristics, and needs to be investigated further.

Keywords: Artificial intelligence; Fetal growth restriction; Neonatal adaptation; Risk prediction.