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Prognostic models for Ebola virus disease derived from data collected at five treatment units in Sierra Leone and Liberia: performance, external validation, and risk visualization

Andres Colubri, Adam C. Levine, Mathew Siakor, Vanessa Wolfman, Mary-Anne Hartley, Tom Sesay, August Felix, Pardis C. Sabeti
doi: https://doi.org/10.1101/294587
Andres Colubri
1Harvard University, Department of Organismic and Evolutionary Biology, Cambridge, MA, USA
4Broad Institute of MIT and Harvard, Cambridge, MA, USA
5Howard Hughes Medical Institute, Chevy Chase, MD, USA
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Adam C. Levine
2Brown University, Warren Alpert School of Medicine. Providence, RI, USA
3International Medical Corps. Los Angeles, CA, USA
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Mathew Siakor
3International Medical Corps. Los Angeles, CA, USA
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Vanessa Wolfman
3International Medical Corps. Los Angeles, CA, USA
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Mary-Anne Hartley
7University of Lausanne, Faculty of Biology and Medicine, Switzerland
8GOAL Global, Dublin, Ireland
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Tom Sesay
9Sierra Leone Ministry of Health and Sanitation
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August Felix
4Broad Institute of MIT and Harvard, Cambridge, MA, USA
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Pardis C. Sabeti
1Harvard University, Department of Organismic and Evolutionary Biology, Cambridge, MA, USA
4Broad Institute of MIT and Harvard, Cambridge, MA, USA
5Howard Hughes Medical Institute, Chevy Chase, MD, USA
6Harvard School of Public Health, Boston, MA, USA
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Abstract

Background The 2014-2016 Ebola Virus Disease (EVD) outbreak highlighted the need for rigorous, rapid, and field-deployable tools to enable case management. We previously introduced an approach for EVD prognosis prediction, using models that can be implemented in the field and updated in light of new data. Here we enhance and validate our methods with the largest published EVD dataset to date. We also present a proof-of-concept medical app that summarizes patient information and offers tailored treatment options using an interactive risk visualization for quick interpretation and decision-making.

Methods and Findings We derived prognosis prediction models for EVD using data from 470 patients admitted to five Ebola treatment units (ETUs) operated by International Medical Corps (IMC) in Liberia and Sierra Leone. We fitted logistic regression models, handled missing data by multiple imputation, and conducted internal validation with bootstrap sampling. We also validated our models with independent datasets from two treatment centers in Sierra Leone comprising 106 patients at Kenema Government Hospital and 158 patients at the GOAL-Mathaska ETU in Port Loko district. We corroborated earlier reports on the importance of viral load and age as mortality predictors and identified jaundice and bleeding to be features with highest predictive value at presentation. Additional clinical symptoms at presentation, although individually weakly correlated with outcome, help broaden sensitivity and refine discrimination of the models. The app provides a visual representation of the predictive outcome as well as attributing clinical protocols adjusted by demographic parameters and prioritized to target the largest contributing factor to overall risk. The app is freely available under the name of Ebola RISK on Google Play and Apple’s App Store.

Conclusions We derived and validated high performance models of EVD prognosis prediction from the largest and most geographically diverse EVD patients available to date. The performance was maintained during external validations on two independent datasets representing different treatment settings and mortality rates, which suggests that the models could be generalized to new populations. These models and derived tools may better inform treatment choices in future EVD outbreaks. The risk visualization app also provides a template to validate additional datasets used in developing novel clinical-decision support systems for EVD and other emerging infectious diseases.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted April 27, 2018.
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Prognostic models for Ebola virus disease derived from data collected at five treatment units in Sierra Leone and Liberia: performance, external validation, and risk visualization
Andres Colubri, Adam C. Levine, Mathew Siakor, Vanessa Wolfman, Mary-Anne Hartley, Tom Sesay, August Felix, Pardis C. Sabeti
bioRxiv 294587; doi: https://doi.org/10.1101/294587
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Prognostic models for Ebola virus disease derived from data collected at five treatment units in Sierra Leone and Liberia: performance, external validation, and risk visualization
Andres Colubri, Adam C. Levine, Mathew Siakor, Vanessa Wolfman, Mary-Anne Hartley, Tom Sesay, August Felix, Pardis C. Sabeti
bioRxiv 294587; doi: https://doi.org/10.1101/294587

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