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Objectives: Early-onset severe preeclampsia is associated with significant maternal and perinatal morbidity and mortality especially in low-resource settings, where women have limited access to antenatal care. This dataset was generated from a retrospective cross-sectional study carried out at Mpilo Central Hospital, covering the period February 1, 2016 to July 30, 2018. The aim of the study was to determine the incidence of early-onset severe preeclampsia and eclampsia, and associated risk factors in a low-resource setting. The reason for examining the incidence of preeclampsia specifically in a low-resource setting; was to document it as women in these settings appear to suffer from poor outcomes. Data description: The dataset contains data of 238 pregnant women who had a diagnosis of early onset severe preeclampsia/eclampsia. There were 243 babies from singleton and twin gestations. There were five sets of twins. There were 21,505 live births during the study period giving an incidence of 1.1%. The dataset contains data on maternal socio-demographic, signs and symptoms, therapeutic interventions and mode of delivery, adverse outcomes characteristics, and fetal characteristics. This large dataset can be used to calculate the incidence and risk factors for adverse maternal and fetal outcomes or develop predictive models in severe preeclampsia/eclampsia. [ABSTRACT FROM AUTHOR]

Hypertensive disorders in pregnancy are a leading cause of maternal and perinatal morbidity and mortality, especially in low-resource settings. Identifying mothers and babies at greatest risk of complications would enable intervention to be targeted to those most likely to benefit from them. However, current risk prediction models have a wide range of sensitivity (42–81%) and specificity (87–92%) indicating that improvements are needed. Furthermore, no predictive models have been developed or evaluated in Zimbabwe. This proposal describes a single centre retrospective cross-sectional study which will address the need to further develop and test statistical risk prediction models for adverse maternal and neonatal outcomes in low-resource settings; this will be the first such research to be carried out in Zimbabwe. Data will be collected on maternal demographics characteristics, outcome of prior pregnancies, past medical history, symptoms and signs on admission, results of biochemical and haematological investigations. Adverse outcome will be defined as a composite of maternal morbidity and mortality and perinatal morbidity and mortality. Association between variables and outcomes will be explored using multivariable logistic regression. Critically, new risk prediction models introduced for our clinical setting may reduce avoidable maternal and neonatal morbidity and mortality at local, national, regional and international level. [ABSTRACT FROM AUTHOR]