The neonatal period is the period in life with the highest risk for mortality. Annually, 2.5 million neonatal deaths and 2.6 million stillbirths occur globally, of which 1.3 million are intrapartum stillbirths. It is estimated that approximately 98% of all neonatal and perinatal deaths occur in low- and middle-income countries. However, almost all the published literature on identifying predictors of fetal and neonatal mortality and risk scoring tools are based on data from high-income countries. Data from low- and middle-income countries are limited to small sample size studies that lack validation with an independent sample. Additionally, machine learning prediction models may perform better than conventional models when applied to large data sets given their ability to delineate complex relationships and identify novel interactions between variables. Although machine learning–based prediction models are expected to perform better with large data sets, this hypothesis has not been convincingly tested with a good quality prospectively collected population database.

We aimed to develop a risk assessment tool for intrapartum stillbirth and neonatal mortality that would include maternal and neonatal variables from a prospective multicountry maternal and neonatal database. We compared various conventional and advanced machine learning–based, analytical modeling methods at specific time points to establish individual predictive accuracies of the models. We tested the hypothesis that intrapartum stillbirth and neonatal mortality risk prediction models that include antenatal and delivery variables provide a high accuracy. Additionally, we also tested whether advanced machine learning–based models have higher predictive accuracy than a conventional logistic regression model.

By: Shukla VV, Eggleston B, Ambalavanan N, McClure EM, Patel A, Mwenechanya M et al.

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