Derivation and Internal Validation of a Mortality Prognostication Machine Learning Model in Ebola Virus Disease Based on Iterative Point-of-Care Biomarkers.

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.

Maternal, fetal, and perinatal outcomes among pregnant women admitted to an Ebola treatment center in the Democratic Republic of Congo, 2018-2020.

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.

Constructing, validating, and updating machine learning models to predict survival in children with Ebola Virus Disease.

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.

Effect of Recombinant Vesicular Stomatitis Virus-Zaire Ebola Virus Vaccination on Ebola Virus Disease Illness and Death, Democratic Republic of the Congo.

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.

Risk Prediction Score for Pediatric Patients with Suspected Ebola Virus Disease.

Rapid diagnostic tools for children with Ebola virus disease (EVD) are needed to expedite isolation and treatment. To evaluate a predictive diagnostic tool, we examined retrospective data (2014-2015) from the International Medical Corps Ebola Treatment Centers in West Africa. We incorporated statistically derived candidate predictors into a 7-point Pediatric Ebola Risk Score. Evidence of bleeding or having known or no known Ebola contacts was positively associated with an EVD diagnosis, whereas abdominal pain was negatively associated. Model discrimination using area under the curve (AUC) was 0.87, which outperforms the World Health Organization criteria (AUC 0.56). External validation, performed by using data from International Medical Corps Ebola Treatment Centers in the Democratic Republic of the Congo during 2018-2019, showed an AUC of 0.70. External validation showed that discrimination achieved by using World Health Organization criteria was similar; however, the Pediatric Ebola Risk Score is simpler to use.