Habgood-Coote, DominicDominicHabgood-CooteWilson, ClareClareWilsonShimizu, ChisatoChisatoShimizuBarendregt, Anouk MAnouk MBarendregtPhilipsen, RiaRiaPhilipsenGalassini, RachelRachelGalassiniCalle, Irene RiveroIrene RiveroCalleWorkman, LesleyLesleyWorkmanAgyeman, Philipp Kwame AbayiePhilipp Kwame AbayieAgyeman0000-0002-8339-5444Ferwerda, GerbenGerbenFerwerdaAnderson, Suzanne TSuzanne TAndersonvan den Berg, J MerlijnJ Merlijnvan den BergEmonts, MariekeMariekeEmontsCarrol, Enitan DEnitan DCarrolFink, Colin GColin GFinkde Groot, RonaldRonaldde GrootHibberd, Martin LMartin LHibberdKanegaye, JohnJohnKanegayeNicol, Mark PMark PNicolPaulus, StéphaneStéphanePaulusPollard, Andrew JAndrew JPollardSalas, AntonioAntonioSalasSecka, FatouFatouSeckaSchlapbach, Luregn JLuregn JSchlapbachTremoulet, Adriana HAdriana HTremouletWalther, MichaelMichaelWaltherZenz, WernerWernerZenzVan der Flier, MichielMichielVan der FlierZar, Heather JHeather JZarKuijpers, TacoTacoKuijpersBurns, Jane CJane CBurnsMartinón-Torres, FedericoFedericoMartinón-TorresWright, Victoria JVictoria JWrightCoin, Lachlan J MLachlan J MCoinCunnington, Aubrey JAubrey JCunningtonHerberg, Jethro AJethro AHerbergLevin, MichaelMichaelLevinKaforou, MyrsiniMyrsiniKaforou2024-10-252024-10-252023-09-08https://boris-portal.unibe.ch/handle/20.500.12422/169382BACKGROUND 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. METHODS 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. FINDINGS 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. CONCLUSIONS 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. FUNDING 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.enRNA-seq Translation to patients biomarkers gene expression host response infectious disease inflammatory disease machine learning multi-class classification point-of-care diagnostics transcriptomics600 - Technology::610 - Medicine & healtharticle10.48350/1855873759751210.1016/j.medj.2023.06.007