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

Collapse authors list.
Habgood-Coote, Dominic, Wilson, Clare, Shimizu, Chisato, Barendregt, Anouk M, Philipsen, Ria, Galassini, Rachel, Calle, Irene Rivero, Workman, Lesley, Agyeman, Philipp KA, Ferwerda, Gerben et al (show 33 more authors) , Anderson, Suzanne T, van den Berg, J Merlijn, Emonts, Marieke, Carrol, Enitan D ORCID: 0000-0001-8357-7726, Fink, Colin G, de Groot, Ronald, Hibberd, Martin L, Kanegaye, John, Nicol, Mark P, Paulus, Stéphane, Pollard, Andrew J, Salas, Antonio, Secka, Fatou, Schlapbach, Luregn J, Tremoulet, Adriana H, Walther, Michael, Zenz, Werner, Pediatric Emergency Medicine Kawasaki Disease Research Group (PE, , UK Kawasaki Genetics consortium, , GENDRES consortium, , EUCLIDS consortium, , PERFORM consortium, , Van der Flier, Michiel, Zar, Heather J, Kuijpers, Taco, Burns, Jane C, Martinón-Torres, Federico, Wright, Victoria J, Coin, Lachlan JM, Cunnington, Aubrey J, Herberg, Jethro A, Levin, Michael and Kaforou, Myrsini (2023) Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature. Med (New York, N.Y.), 4 (9). S2666-6340(23)00194-0-S2666-6340(23)00194-0.

PDF Diagnosis of childhood febrile illness using a multi-class blood RNA Habgood-Coote Cell Med 2023.pdf - Open Access published version Download (3MB) | Preview

Abstract

<h4>Background</h4>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.<h4>Methods</h4>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.<h4>Findings</h4>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.<h4>Conclusions</h4>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.<h4>Funding</h4>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.

Item Type:	Article
Uncontrolled Keywords:	EUCLIDS consortium, GENDRES consortium, Pediatric Emergency Medicine Kawasaki Disease Research Group (PEMKDRG), PERFORM consortium, UK Kawasaki Genetics consortium
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences
Depositing User:	Symplectic Admin
Date Deposited:	25 Aug 2023 09:56
Last Modified:	18 Oct 2023 13:56
DOI:	10.1016/j.medj.2023.06.007
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3172347