biostudies-literatureKo ERUS NIH NIAIDFIC NIH HHSUS Army Medical Research and Materiel Command22554https://www.ebi.ac.uk/biostudies/studies/S-EPMC10728077biostudies-literatureUnknown13(1)Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.Scientific reportsHost-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance.PMC10728077R01 TW009237W81XWH-16-C-0147R01TW009237Nagahawatte ACrump JAHenao RTillekeratne LGBodinayake CKGinsburg GSTsalik ELSuchindran SReller MESchully KLDevasiri VDe Silva ADMadut DNicholson BMiller CKurukulasooriya RKo ERPetzold EKato CMcClain MTGalloway RDumler JSLwezaula BFMinogue TDBurke TWBlatt AClark DVMaro VPWoods CWKodikara-Arachichi WRubach MPfalseHost-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance.Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76-0.90) with overall accuracy of 81.6% (95% CI 72.7-88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.2023-01-01T00:00:00Z2023 Dec2024-11-09T15:21:11.518Z2024-11-09T15:21:11.518ZS-EPMC107280773811053410.1038/s41598-023-49734-6