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Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

2023; Elsevier BV; Volume: 4; Issue: 9 Linguagem: Inglês

10.1016/j.medj.2023.06.007

2666-6359

Dominic Habgood-Coote, Clare Wilson, Chisato Shimizu, Anouk M. Barendregt, Ria Philipsen, Rachel Galassini, Irene Rivero‐Calle, Lesley Workman, Philipp Agyeman, Gerben Ferwerda, Suzanne T. Anderson, J. Merlijn van den Berg, Marieke Emonts, Enitan D. Carrol, Colin G. Fink, Ronald de Groot, Martin L. Hibberd, John T. Kanegaye, Mark P. Nicol, Stéphane Paulus, Andrew J. Pollard, Antonio Salas, Fatou Secka, Luregn J. Schlapbach, Adriana Tremoulet, Michael Walther, Werner Zenz, Michiel van der Flier, Heather J. Zar, Taco Kuijpers, Jane C. Burns, Federico Martinón‐Torres, Victoria Wright, Lachlan Coin, Aubrey J. Cunnington, Jethro Herberg, Michael Levin, Myrsini Kaforou,

Tuberculosis Research and Epidemiology

Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood.A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children.We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures.Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis.European Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC.