TY - JOUR
T1 - Host-response transcriptional biomarkers accurately discriminate bacterial and viral infections of global relevance
AU - Ko, Emily R.
AU - Reller, Megan E.
AU - Tillekeratne, L. Gayani
AU - Bodinayake, Champica K.
AU - Miller, Cameron
AU - Burke, Thomas W.
AU - Henao, Ricardo
AU - McClain, Micah T.
AU - Suchindran, Sunil
AU - Nicholson, Bradly
AU - Blatt, Adam
AU - Petzold, Elizabeth
AU - Tsalik, Ephraim L.
AU - Nagahawatte, Ajith
AU - Devasiri, Vasantha
AU - Rubach, Matthew P.
AU - Maro, Venance P.
AU - Lwezaula, Bingileki F.
AU - Kodikara-Arachichi, Wasantha
AU - Kurukulasooriya, Ruvini
AU - De Silva, Aruna D.
AU - Clark, Danielle V.
AU - Schully, Kevin L.
AU - Madut, Deng
AU - Dumler, J. Stephen
AU - Kato, Cecilia
AU - Galloway, Renee
AU - Crump, John A.
AU - Ginsburg, Geoffrey S.
AU - Minogue, Timothy D.
AU - Woods, Christopher W.
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12
Y1 - 2023/12
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85180254515
U2 - 10.1038/s41598-023-49734-6
DO - 10.1038/s41598-023-49734-6
M3 - Article
C2 - 38110534
AN - SCOPUS:85180254515
SN - 2045-2322
VL - 13
JO - Scientific Reports
JF - Scientific Reports
IS - 1
M1 - 22554
ER -