One size fits all? The role of social determinants of health on AI-driven CTG intrapartum decision-support tools in term babies

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Key objectives: to analyse the interaction between ethnicity and other social determinants of health and adverse outcomes during labour in term babies; to use ethnicity data and other social determinants of health in AI-driven CTG intrapartum decision-support tools to predict perinatal adverse events; to understand the experience of women from underrepresented backgrounds and clinicians using AI-driven decision aid tool for intrapartum risk assessment. Clinical need: Hypoxia, before or during labour remains a significant challenge to clinical teams, from its origin, detection, prevention, and treatment. Moreover, labour presents several challenges to mother and fetus. The long-lasting physical and mental challenge has often been compared to a marathon and the effort required giving rise to its name, 'labour'. Physiologically, contractions compress blood vessels that lead to an intermittent myometrial hypoxia. However, if this hypoxia is prolonged, will contribute to dysfunctional (slow to progress) labours. Hypoxia in labour is still unknown, and is responsible for adverse perinatal outcomes in women and babies and unplanned surgical interventions (Wray et al. 2021). Intrapartum hypoxic ischaemic injury is a major contributor to stillbirths, neonatal encephalopathy and mortality, and long term neurodevelopmental sequelae worldwide (Lovers et al. 2022). Furthermore, evidence shows that women from the poorest backgrounds and mothers from Black, Asian and Minority Ethnic groups are at higher risk of their baby dying in the womb or soon after birth (Draper et al. 2018). Consequently, these adverse perinatal outcomes have not only a detrimental impact on women's, infants and families' lives but also have an effect on human, material and economic resources. Hence, the prevention of hypoxia is a major public health issue and a significant health priority.The use of cardiotocography (CTG) has become a standard technique to monitor the state of the fetus during labour; however, the major problems with fetal heart rate (FHR) monitoring relate to the reading and interpretation of the CTG tracings (Schiermeier et al. 2008). Controversies around routine CTG monitoring exist nowadays due to the their high false-positive rate and increment in clinical interventions without improving neonatal outcomes (Alfirevic et al. 2013). Nevertheless intrapartum CTG is currently the only method of surveillance approved by national societies (Georgieva et al. 2014), particularly for high risk pregnant women ( Ayres-de-Campos et al. 2015). The need for tackling this public health issue has been acknowledged through the development of several national campaigns and reports, such as Saving Babies Lives, the NHS Long Term Plan and the Ockenden report. The Oxford Labour Monitoring (University of Oxford) is a specialist team developing data-driven cardiotocography (CTG) systems/software to continuously assess fetal wellbeing at the onset of and during term labour. The OxSys, an AI-driven decision support tool in labour serves as a screening test to identify high risk or unfit babies at the onset of labour. This tool is based on a statistical prognostic model that takes into consideration antenatal and intrapartum risk factors as well as using computerized CTG to predict the risk of experiencing perinatal adverse outcomes.