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Objective: This study aims to investigate maternal, fetal, and perinatal outcomes during the 2018–2020 Ebola outbreak in Democratic Republic of Congo (DRC). Methods: Mortality between pregnant and non-pregnant women of reproductive age admitted to DRC's Mangina Ebola treatment center (ETC) were compared using propensity score matching. Propensity scores were calculated using age, initial Ebola viral load, Ebola vaccination status, and investigational therapeutic. Additionally, fetal and perinatal outcomes of pregnancies were also described. Results: Twenty-seven pregnant women were admitted to the Mangina ETC during December 2018—January 2020 among 162 women of childbearing age. We found no evidence of increase mortality among pregnant women compared to non-pregnant women (relative risk:1.0, 95%CI: 0.58–1.72). Among surviving mothers, pregnancy outcomes were poor with at least 58% (11/19) experiencing loss of pregnancy while 16% (3/19) were discharged with viable pregnancy. Two mothers with viable pregnancies were vaccinated, and all received investigational therapeutics. Two live births occurred, with one infant surviving after the infant and mother received an investigational post-exposure prophylaxis and Ebola therapeutic respectively. Conclusions: Pregnancy was not associated with increased mortality among women with EVD in the Mangina ETC. Fetal and perinatal outcomes remained poor in pregnancies complicated by EVD, though novel therapeutics may have potential for improving these outcomes. [ABSTRACT FROM AUTHOR]

Background: Ebola Virus Disease (EVD) causes high case fatality rates (CFRs) in young children, yet there are limited data focusing on predicting mortality in pediatric patients. Here we present machine learning-derived prognostic models to predict clinical outcomes in children infected with Ebola virus. Methods: Using retrospective data from the Ebola Data Platform, we investigated children with EVD from the West African EVD outbreak in 2014–2016. Elastic net regularization was used to create a prognostic model for EVD mortality. In addition to external validation with data from the 2018–2020 EVD epidemic in the Democratic Republic of the Congo (DRC), we updated the model using selected serum biomarkers. Findings: Pediatric EVD mortality was significantly associated with younger age, lower PCR cycle threshold (Ct) values, unexplained bleeding, respiratory distress, bone/muscle pain, anorexia, dysphagia, and diarrhea. These variables were combined to develop the newly described EVD Prognosis in Children (EPiC) predictive model. The area under the receiver operating characteristic curve (AUC) for EPiC was 0.77 (95% CI: 0.74–0.81) in the West Africa derivation dataset and 0.76 (95% CI: 0.64–0.88) in the DRC validation dataset. Updating the model with peak aspartate aminotransferase (AST) or creatinine kinase (CK) measured within the first 48 hours after admission increased the AUC to 0.90 (0.77–1.00) and 0.87 (0.74–1.00), respectively. Conclusion: The novel EPiC prognostic model that incorporates clinical information and commonly used biochemical tests, such as AST and CK, can be used to predict mortality in children with EVD. Author summary: Although case fatality rates remain high, there are limited data on predicting mortality in children with Ebola Virus Disease (EVD). Furthermore, challenges in predicting EVD outcomes using clinical and laboratory data highlight the need for the development and validation of pediatric predictive models. The novel EVD Prognosis in Children (EPiC) model uses clinical and biochemical information, such as AST and CK, to predict mortality in infected children. While few prognostic models or scoring systems have been developed to predict clinical outcomes of EVD, the majority of them were limited in geographical and temporal scope having been derived using data from one location. As such, the EPiC model is the first externally validated model for the prognosis of pediatric EVD using diverse datasets from geographically and temporally separate outbreaks. This model can be easily applied by bedside clinicians to assess pediatric patients at risk for death and help to allocate resources accordingly. [ABSTRACT FROM AUTHOR]