Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data

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Title: Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data
Authors: Johnston, I
Hoffmann, T
Greenbury, S
Cominetti, O
Jallow, M
Kwiatkowski, D
Barahona, M
Jones, N
Casals-Pascual, C
Item Type: Journal Article
Abstract: More than 400,000 deaths from severe malaria (SM) are reported every year, mainly in African children. The diversity of clinical presentations associated with SM indicates important differences in disease pathogenesis that require specific treatment, and this clinical heterogeneity of SM remains poorly understood. Here, we apply tools from machine learning and model-based inference to harness large-scale data and dissect the heterogeneity in patterns of clinical features associated with SM in 2904 Gambian children admitted to hospital with malaria. This quantitative analysis reveals features predicting the severity of individual patient outcomes, and the dynamic pathways of SM progression, notably inferred without requiring longitudinal observations. Bayesian inference of these pathways allows us assign quantitative mortality risks to individual patients. By independently surveying expert practitioners, we show that this data-driven approach agrees with and expands the current state of knowledge on malaria progression, while simultaneously providing a data-supported framework for predicting clinical risk.
Issue Date: 10-Jul-2019
Date of Acceptance: 6-Jun-2019
ISSN: 2398-6352
Publisher: Nature Research (part of Springer Nature)
Journal / Book Title: npj Digital Medicine
Volume: 2
Copyright Statement: © 2019 The Author(s). Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit
Sponsor/Funder: Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: EP/N014529/1
Publication Status: Published
Article Number: 63
Appears in Collections:Mathematics
Applied Mathematics and Mathematical Physics

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