Blood Test Could Identify Which Patients Need Antibiotics
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By LabMedica International staff writers Posted on 31 Jan 2016 |

Image: Molecular surface of the capsid of human rhinovirus, one of the viruses which cause acute respiratory infections (Photo courtesy of A. J. Cann).
Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical car and despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics.
Misuse of antibiotics is a key contributor to antibiotic resistance; if a patient takes antibiotics for a viral infection, the drug will still attack bacteria in the body, but it will attack healthy or beneficial bacteria. This can cause antibiotic-resistant properties that can be passed on to other bacteria.
Scientists at Duke University (Durham, NC, USA) and their colleagues investigated whether host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. The 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.
By measuring patients' gene expression profiles from blood samples, the team found they could use the previously identified gene signatures to correctly identify patients with influenza viruses, rhinovirus, various streptococci bacteria and other common infections with 87% accuracy. This Overall accuracy from 238 of 273 subjects was concordant with clinical adjudication, which was more accurate than procalcitonin (78%), and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed were externally validated in five publicly available data sets. A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens.
The authors concluded that by applying the ARI classifiers they could define 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. They note that there is one major limitation, however as at present as it takes around 10 hours to assess a patient's gene expression profile, but the team says they are in the process of working with developers to create a one hour test that can be used by clinicians.
Christopher Woods, MD, MPH, a professor of medicine and senior author of the study, said, “The ideal scenario, should this test ultimately be approved for broad use, is you would go to the doctor's office and receive your results by the time you meet with your provider. We are working to develop a test that could be run in most clinical labs on existing equipment. We believe this could have a real impact on the appropriate use of antibiotics and guide the use of antiviral treatments in the future.” The study was published on January 20, 2016, in the journal Science Translational Medicine.
Related Links:
Duke University
Misuse of antibiotics is a key contributor to antibiotic resistance; if a patient takes antibiotics for a viral infection, the drug will still attack bacteria in the body, but it will attack healthy or beneficial bacteria. This can cause antibiotic-resistant properties that can be passed on to other bacteria.
Scientists at Duke University (Durham, NC, USA) and their colleagues investigated whether host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. The 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.
By measuring patients' gene expression profiles from blood samples, the team found they could use the previously identified gene signatures to correctly identify patients with influenza viruses, rhinovirus, various streptococci bacteria and other common infections with 87% accuracy. This Overall accuracy from 238 of 273 subjects was concordant with clinical adjudication, which was more accurate than procalcitonin (78%), and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed were externally validated in five publicly available data sets. A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens.
The authors concluded that by applying the ARI classifiers they could define 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. They note that there is one major limitation, however as at present as it takes around 10 hours to assess a patient's gene expression profile, but the team says they are in the process of working with developers to create a one hour test that can be used by clinicians.
Christopher Woods, MD, MPH, a professor of medicine and senior author of the study, said, “The ideal scenario, should this test ultimately be approved for broad use, is you would go to the doctor's office and receive your results by the time you meet with your provider. We are working to develop a test that could be run in most clinical labs on existing equipment. We believe this could have a real impact on the appropriate use of antibiotics and guide the use of antiviral treatments in the future.” The study was published on January 20, 2016, in the journal Science Translational Medicine.
Related Links:
Duke University
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