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dc.contributor.authorGenisca, Alicia E
dc.contributor.authorButler, Kelsey
dc.contributor.authorGainey, Monique
dc.contributor.authorChu, Tzu-Chun
dc.contributor.authorHuang, Lawrence
dc.contributor.authorMbong, Eta N
dc.contributor.authorKennedy, Stephen B
dc.contributor.authorLaghari, Razia
dc.contributor.authorNganga, Fiston
dc.contributor.authorMuhayangabo, Rigobert F
dc.contributor.authorVaishnav, Himanshu
dc.contributor.authorPerera, Shiromi M
dc.contributor.authorAdeniji, Moyinoluwa
dc.contributor.authorLevine, Adam C
dc.contributor.authorMichelow, Ian C
dc.contributor.authorColubri, Andrés
dc.date.accessioned2022-12-13T18:21:42Z
dc.date.available2022-12-13T18:21:42Z
dc.date.issued2022-10-12
dc.identifier.citationGenisca 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.en_US
dc.identifier.eissn1935-2735
dc.identifier.doi10.1371/journal.pntd.0010789en_US
dc.identifier.pmid36223331
dc.identifier.urihttp://hdl.handle.net/20.500.14038/51452
dc.description.abstractBackground: 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.en_US
dc.language.isoenen_US
dc.relation.ispartofPLOS Neglected Tropical Diseasesen_US
dc.relation.urlhttps://doi.org/10.1371/journal.pntd.0010789en_US
dc.rightsCopyright: © 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 Internationalen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPediatricsen_US
dc.subjectAfricaen_US
dc.subjectBiomarkersen_US
dc.subjectForecastingen_US
dc.subjectMedical risk factorsen_US
dc.subjectHemorrhageen_US
dc.subjectMyalgiaen_US
dc.subjectEbola hemorrhagic feveren_US
dc.titleConstructing, validating, and updating machine learning models to predict survival in children with Ebola Virus Diseaseen_US
dc.typeJournal Articleen_US
dc.source.journaltitlePLoS neglected tropical diseases
dc.source.volume16
dc.source.issue10
dc.source.beginpagee0010789
dc.source.endpage
dc.source.countryUnited States
dc.source.countryUnited States
dc.identifier.journalPLoS neglected tropical diseases
refterms.dateFOA2022-12-13T18:21:43Z
dc.contributor.departmentMicrobiology and Physiological Systemsen_US
dc.contributor.departmentMorningside Graduate School of Biomedical Sciencesen_US
dc.contributor.departmentProgram in Bioinformatics and Integrative Biologyen_US


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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
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