Department of Infectious Disease Epidemiology, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany.
German Center for Infection Research (DZIF), Hamburg-Borstel-Lübeck-Riems, Germany.
J Infect Dis. 2020 Mar 16;221(7):1098-1106. doi: 10.1093/infdis/jiz587.
Malaria presents with unspecific clinical symptoms that frequently overlap with other infectious diseases and is also a risk factor for coinfections, such as non-Typhi Salmonella. Malaria rapid diagnostic tests are sensitive but unable to distinguish between an acute infection requiring treatment and asymptomatic malaria with a concomitant infection. We set out to test whether cytokine profiles could predict disease status and allow the differentiation between malaria and a bacterial bloodstream infection.
We created a classification model based on cytokine concentration levels of pediatric inpatients with either Plasmodium falciparum malaria or a bacterial bloodstream infection using the Luminex platform. Candidate markers were preselected using classification and regression trees, and the predictive strength was calculated through random forest modeling.
Analyses revealed that a combination of 7-15 cytokines exhibited a median disease prediction accuracy of 88% (95th percentile interval, 73%-100%). Haptoglobin, soluble Fas-Ligand, and complement component C2 were the strongest single markers with median prediction accuracies of 82% (with 95th percentile intervals of 71%-94%, 62%-94%, and 62%-94%, respectively).
Cytokine profiles possess good median disease prediction accuracy and offer new possibilities for the development of innovative point-of-care tests to guide treatment decisions in malaria-endemic regions.
疟疾的临床表现缺乏特异性，常与其他传染病重叠，也是合并感染的危险因素，如非伤寒沙门氏菌。疟疾快速诊断检测具有较高的敏感性，但无法区分需要治疗的急性感染和伴有合并感染的无症状疟疾。我们旨在测试细胞因子谱是否可以预测疾病状态，并区分疟疾和细菌性血流感染。
我们使用 Luminex 平台，为患有恶性疟原虫疟疾或细菌性血流感染的儿科住院患者创建了基于细胞因子浓度水平的分类模型。使用分类和回归树预先选择候选标志物，并通过随机森林建模计算预测强度。
分析表明，7-15 种细胞因子的组合具有 88%的中位数疾病预测准确性（95%置信区间，73%-100%）。触珠蛋白、可溶性 Fas 配体和补体成分 C2 是最强的单个标志物，中位数预测准确性分别为 82%（95%置信区间分别为 71%-94%、62%-94%和 62%-94%）。
细胞因子谱具有良好的中位数疾病预测准确性，为开发创新的即时检测技术提供了新的可能性，以指导疟疾流行地区的治疗决策。
