Introduction
Globally, hypoxia remains a major contributor to stillbirth, hypoxic ischaemic encephalopathy and cerebral palsy. For parents and families, the psychosocial and financial impact of these complications are profound and long-lasting. The majority of these catastrophic events occur despite a lack of obvious risk factors.1 This problem is significant and pressing, with the Royal College of Obstetricians and Gynaecologists, Gates Foundation, The Lancet and WHO urging focused research in this area. Indeed, a recent major 2017 UK report (‘Each Baby Counts’) of stillbirths, neonatal deaths and perinatal brain injury occurring has set an ambitious 50% reduction target by 2020.2
One prerequisite of any strategy to reduce adverse outcomes is the need to identify an at-risk population of fetuses. However, there is often lack of clarity of the population being screened and the perinatal outcomes chosen. Furthermore, clinically plausible and accurate interpretation of the relationship between risk variables and health outcomes is vital to ensure the robustness of any predictive model.3 The development of risk algorithms and predictive models using both ultrasound and demographic variables to enable risk stratification and individualised care is an increasing focus of research to reduce stillbirth and other adverse outcomes in high-income country settings.4 The accuracy of these models depends on careful consideration of the association between risk factors and outcomes, and importantly how these factors interact with and on occasion, confound each other.
The cerebroplacental ratio (CPR) is the ratio of the middle cerebral artery pulsatility index (MCA PI) divided by the umbilical artery pulsatility index (UA PI) and is now shown to be a possible marker of suboptimal fetal growth regardless of gestation.5–7 A low CPR is associated with a variety of adverse perinatal outcomes including stillbirth, intrapartum fetal compromise and acidosis at birth, a low Apgar score and neonatal unit admission regardless of gestational age or weight.8–11 The CPR is now increasingly being incorporated into clinical practice despite its relatively poor performance as a screening test for adverse perinatal outcomes.9 12–14 Previously, we have shown that both the CPR and estimated fetal weight (EFW) identified distinct at-risk cohorts and that a model incorporating both these factors improved the predictive capability for adverse perinatal outcomes.15 Others16 17 have used a larger number of variables including the CPR, fetal gender, parity, maternal age, EFW and gestational age at birth to develop models for prediction of adverse pregnancy outcomes.
The aim of this study was to develop a predictive model using a range of maternal, pregnancy, intrapartum and ultrasound variables for a composite of severe adverse neonatal outcomes (SANO) for term infants.