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Background Although multiple prognostic models exist for Ebola virus disease mortality, few incorporate biomarkers, and none has used longitudinal point-of-care serum testing throughout Ebola treatment center care. Methods This retrospective study evaluated adult patients with Ebola virus disease during the 10th outbreak in the Democratic Republic of Congo. Ebola virus cycle threshold (Ct; based on reverse transcriptase polymerase chain reaction) and point-of-care serum biomarker values were collected throughout Ebola treatment center care. Four iterative machine learning models were created for prognosis of mortality. The base model used age and admission Ct as predictors. Ct and biomarkers from treatment days 1 and 2, days 3 and 4, and days 5 and 6 associated with mortality were iteratively added to the model to yield mortality risk estimates. Receiver operating characteristic curves for each iteration provided period-specific areas under curve with 95% CIs. Results Of 310 cases positive for Ebola virus disease, mortality occurred in 46.5%. Biomarkers predictive of mortality were elevated creatinine kinase, aspartate aminotransferase, blood urea nitrogen (BUN), alanine aminotransferase, and potassium; low albumin during days 1 and 2; elevated C-reactive protein, BUN, and potassium during days 3 and 4; and elevated C-reactive protein and BUN during days 5 and 6. The area under curve substantially improved with each iteration: base model, 0.74 (95% CI,.69–.80); days 1 and 2, 0.84 (95% CI,.73–.94); days 3 and 4, 0.94 (95% CI,.88–1.0); and days 5 and 6, 0.96 (95% CI,.90–1.0). Conclusions This is the first study to utilize iterative point-of-care biomarkers to derive dynamic prognostic mortality models. This novel approach demonstrates that utilizing biomarkers drastically improved prognostication up to 6 days into patient care. [ABSTRACT FROM AUTHOR]

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]

Populations affected by humanitarian crises and emerging infectious disease outbreaks may have unique concerns and experiences that influence their perceptions toward vaccines. In March 2021, we conducted a survey to examine the perceptions toward COVID-19 vaccines and identify the factors associated with vaccine intention among 631 community members (CMs) and 438 healthcare workers (HCWs) affected by the 2018–2020 Ebola Virus Disease outbreak in North Kivu, Democratic Republic of the Congo. A multivariable logistic regression was used to identify correlates of vaccine intention. Most HCWs (81.7%) and 53.6% of CMs felt at risk of contracting COVID-19; however, vaccine intention was low (27.6% CMs; 39.7% HCWs). In both groups, the perceived risk of contracting COVID-19, general vaccine confidence, and male sex were associated with the intention to get vaccinated, with security concerns preventing vaccine access being negatively associated. Among CMs, getting the Ebola vaccine was associated with the intention to get vaccinated (RR 1.43, 95% CI 1.05–1.94). Among HCWs, concerns about new vaccines' safety and side effects (OR 0.72, 95% CI 0.57–0.91), religion's influence on health decisions (OR 0.45, 95% CI 0.34–0.61), security concerns (OR 0.52, 95% CI 0.37–0.74), and governmental distrust (OR 0.50, 95% CI 0.35–0.70) were negatively associated with vaccine perceptions. Enhanced community engagement and communication that address this population's concerns could help improve vaccine perceptions and vaccination decisions. These findings could facilitate the success of vaccine campaigns in North Kivu and similar settings. [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]

We conducted a retrospective cohort study to assess the effect vaccination with the live-attenuated recombinant vesicular stomatitis virus-Zaire Ebola virus vaccine had on deaths among patients who had laboratory-confirmed Ebola virus disease (EVD). We included EVD-positive patients coming to an Ebola Treatment Center in eastern Democratic Republic of the Congo during 2018-2020. Overall, 25% of patients vaccinated before symptom onset died compared with 63% of unvaccinated patients. Vaccinated patients reported fewer EVD-associated symptoms, had reduced time to clearance of viral load, and had reduced length of stay at the Ebola Treatment Center. After controlling for confounders, vaccination was strongly associated with decreased deaths. Reduction in deaths was not affected by timing of vaccination before or after EVD exposure. These findings support use of preexposure and postexposure recombinant vesicular stomatitis virus-Zaire Ebola virus vaccine as an intervention associated with improved death rates, illness, and recovery time among patients with EVD. [ABSTRACT FROM AUTHOR]