Constructing, validating, and updating machine learning models to predict survival in children with Ebola Virus Disease
Authors
Genisca, Alicia EButler, Kelsey
Gainey, Monique
Chu, Tzu-Chun
Huang, Lawrence
Mbong, Eta N
Kennedy, Stephen B
Laghari, Razia
Nganga, Fiston
Muhayangabo, Rigobert F
Vaishnav, Himanshu
Perera, Shiromi M
Adeniji, Moyinoluwa
Levine, Adam C
Michelow, Ian C
Colubri, Andrés
UMass Chan Affiliations
Microbiology and Physiological SystemsMorningside Graduate School of Biomedical Sciences
Program in Bioinformatics and Integrative Biology
Document Type
Journal ArticlePublication Date
2022-10-12Keywords
PediatricsAfrica
Biomarkers
Forecasting
Medical risk factors
Hemorrhage
Myalgia
Ebola hemorrhagic fever
Metadata
Show full item recordAbstract
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.Source
Genisca AE, Butler K, Gainey M, Chu TC, Huang L, Mbong EN, Kennedy SB, Laghari R, Nganga F, Muhayangabo RF, Vaishnav H, Perera SM, Adeniji M, Levine AC, Michelow IC, Colubri A. Constructing, validating, and updating machine learning models to predict survival in children with Ebola Virus Disease. PLoS Negl Trop Dis. 2022 Oct 12;16(10):e0010789. doi: 10.1371/journal.pntd.0010789. PMID: 36223331; PMCID: PMC9555640.DOI
10.1371/journal.pntd.0010789Permanent Link to this Item
http://hdl.handle.net/20.500.14038/51452PubMed ID
36223331Rights
Copyright: © 2022 Genisca et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.; Attribution 4.0 InternationalDistribution License
http://creativecommons.org/licenses/by/4.0/ae974a485f413a2113503eed53cd6c53
10.1371/journal.pntd.0010789
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Except where otherwise noted, this item's license is described as Copyright: © 2022 Genisca et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.; Attribution 4.0 International