The Journal of infectious diseases · 2020
Cytokine Profile Distinguishes Children With Plasmodium falciparum Malaria From Those With Bacterial Blood Stream Infections.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Struck Nicole S, Zimmermann Marlow, Krumkamp Ralf, Lorenz Eva, Jacobs Thomas, Rieger Toni, Wurr Stephanie, Günther Stephan, Gyau Boahen Kennedy, Marks Florian, Sarpong Nimako, Owusu-Dabo Ellis, May Jürgen, Eibach Daniel
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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).
CONCLUSIONS: 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.