Journal article
Host gene expression classifiers diagnose acute respiratory illness etiology
Science translational medicine, v 8(322), pp 322ra11-322ra11
20 Jan 2016
PMID: 26791949
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.
Metrics
Details
- Title
- Host gene expression classifiers diagnose acute respiratory illness etiology
- Creators
- Ephraim L Tsalik - Duke UniversityRicardo Henao - Duke UniversityMarshall Nichols - Duke UniversityThomas Burke - Duke UniversityEmily R Ko - Duke Regional HospitalMicah T McClain - Duke UniversityLori L Hudson - Duke UniversityAnna Mazur - Duke UniversityDebra H Freeman - Duke UniversityTim Veldman - Duke UniversityRaymond J Langley - Lovelace Respiratory Research InstituteEugenia B Quackenbush - University of North Carolina at Chapel HillSeth W Glickman - University of North Carolina at Chapel HillCharles B Cairns - University of North Carolina at Chapel HillAnja K Jaehne - Henry Ford HospitalEmanuel P Rivers - Henry Ford HospitalRonny M Otero - Henry Ford HospitalAimee K Zaas - Duke UniversityStephen F Kingsmore - Rady Children's Hospital-San DiegoJoseph Lucas - Duke UniversityVance G Fowler, Jr - Duke UniversityLawrence Carin - Duke UniversityGeoffrey S Ginsburg - Duke UniversityChristopher W Woods - Duke University
- Publication Details
- Science translational medicine, v 8(322), pp 322ra11-322ra11
- Publisher
- American Association for the Advancement of Science (AAAS)
- Grant note
- IK2 CX000530 / CSRD VA UM1 AI104681 / NIAID NIH HHS 1IK2CX000611 / CSRD VA 266200400064C / PHS HHS IK2 CX000611 / CSRD VA U01 AI066569 / NIAID NIH HHS U01AI066569 / NIAID NIH HHS P20RR016480 / NCRR NIH HHS 1IK2CX000530 / CSRD VA P20 RR016480 / NCRR NIH HHS K24 AI093969 / NIAID NIH HHS K24-AI093969 / NIAID NIH HHS
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Medicine
- Web of Science ID
- WOS:000368511300004
- Scopus ID
- 2-s2.0-84955579105
- Other Identifier
- 991021448164004721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Cell Biology
- Medicine, Research & Experimental