Cross-validated prediction model for severe adverse neonatal outcomes in a term, non-anomalous, singleton cohort.

OBJECTIVE

The aim of this study was to develop a predictive model using maternal, intrapartum and ultrasound variables for a composite of severe adverse neonatal outcomes (SANO) in term infants.

DESIGN

Prospectively collected observational study. Mixed effects generalised linear models were used for modelling. Internal validation was performed using the K-fold cross-validation technique.

SETTING

This was a study of women that birthed at the Mater Mother's Hospital in Brisbane, Australia between January 2010 and April 2017.

PATIENTS

We included all term, non-anomalous singleton pregnancies that had an ultrasound performed between 36 and 38 weeks gestation and had recordings for the umbilical artery pulsatility index, middle cerebral artery pulsatility index and the estimated fetal weight (EFW).

MAIN OUTCOME MEASURES

The components of the SANO were: severe acidosis arterial, admission to the neonatal intensive care unit, Apgar score of ≤3 at 5 min or perinatal death.

RESULTS

There were 5439 women identified during the study period that met the inclusion criteria, with 11.7% of this cohort having SANO. The final generalised linear mixed model consisted of the following variables: maternal ethnicity, socioeconomic score, nulliparity, induction of labour, method of birth and z-scores for EFW and cerebroplacental ratio. The final model had an area under the receiver operating characteristic curve of 0.71.

CONCLUSIONS

The results of this study demonstrate it is possible to predict infants that are at risk of SANO at term with moderate accuracy using a combination of maternal, intrapartum and ultrasound variables. Cross-validation analysis suggests a high calibration of the model.