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

Abstract

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.