Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data.,npj Digital Medicine

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Precision identification of high-risk phenotypes and progression pathways in severe malaria without requiring longitudinal data.
npj Digital Medicine ( IF 12.4 ) Pub Date : 2019-07-10 , DOI: 10.1038/s41746-019-0140-y
Iain G Johnston _{1,

2,

3} , Till Hoffmann ₄ , Sam F Greenbury _{2,

4} , Ornella Cominetti ₅ , Muminatou Jallow ₆ , Dominic Kwiatkowski ₇ , Mauricio Barahona _{2,

4} , Nick S Jones _{2,

4} , Climent Casals-Pascual _{7,

8}

Affiliation

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.

中文翻译：

无需纵向数据即可精确识别重症疟疾的高危表型和进展途径。

据报道，每年有超过 400,000 人死于严重疟疾 (SM)，主要是非洲儿童。与 SM 相关的临床表现的多样性表明需要特定治疗的疾病发病机制存在重要差异，而 SM 的这种临床异质性仍然知之甚少。在这里，我们应用来自机器学习和基于模型的推理的工具来利用大规模数据，并剖析 2904 名因疟疾住院的冈比亚儿童与 SM 相关的临床特征模式的异质性。这种定量分析揭示了预测个体患者结果严重性的特征，以及 SM 进展的动态途径，特别是在不需要纵向观察的情况下推断。这些途径的贝叶斯推断使我们能够将定量的死亡风险分配给个体患者。通过独立调查专家从业人员，我们表明这种数据驱动的方法符合并扩展了当前关于疟疾进展的知识状态，同时为预测临床风险提供了数据支持的框架。

更新日期：2019-11-18

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