Validation of a Host Gene Expression Test for Bacterial/Viral Discrimination in Immunocompromised Hosts.



Host gene expression has emerged as a complementary strategy to pathogen detection tests for the discrimination of bacterial and viral infection. The impact of immunocompromise on host-response tests remains unknown. We evaluated a host-response test discriminating bacterial, viral, and noninfectious conditions in immunocompromised subjects.


An 81-gene signature was measured using real-time-polymerase chain reaction in subjects with immunocompromise (chemotherapy, solid-organ transplant, immunomodulatory agents, AIDS) with bacterial infection, viral infection, or noninfectious illness. A regularized logistic regression model trained in immunocompetent subjects was used to estimate the likelihood of each class in immunocompromised subjects.


Accuracy in the 136-subject immunocompetent training cohort was 84.6% for bacterial versus nonbacterial discrimination and 80.8% for viral versus nonviral discrimination. Model validation in 134 immunocompromised subjects showed overall accuracy of 73.9% for bacterial infection (P = .04 relative to immunocompetent subjects) and 75.4% for viral infection (P = .30). A scheme reporting results by quartile improved test utility. The highest probability quartile ruled-in bacterial and viral infection with 91.4% and 84.0% specificity, respectively. The lowest probability quartile ruled-out infection with 90.1% and 96.4% sensitivity for bacterial and viral infection, respectively. Performance was independent of the type or number of immunocompromising conditions.


A host gene expression test discriminated bacterial, viral, and noninfectious etiologies at a lower overall accuracy in immunocompromised patients compared with immunocompetent patients, although this difference was only significant for bacterial infection classification. With modified interpretive criteria, a host-response strategy may offer clinically useful diagnostic information for patients with immunocompromise.





Published Version (Please cite this version)


Publication Info

Mahle, Rachael E, Sunil Suchindran, Ricardo Henao, Julie M Steinbrink, Thomas W Burke, Micah T McClain, Geoffrey S Ginsburg, Christopher W Woods, et al. (2021). Validation of a Host Gene Expression Test for Bacterial/Viral Discrimination in Immunocompromised Hosts. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 73(4). pp. 605–613. 10.1093/cid/ciab043 Retrieved from

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Ricardo Henao

Associate Professor in Biostatistics & Bioinformatics

Julie Steinbrink

Assistant Professor of Medicine

I am a transplant infectious diseases physician. My clinical care focuses on the management of infections in immunocompromised patients, including solid organ and bone marrow transplant recipients, as well as cancer patients. My research focuses on developing noninvasive biomarker diagnostics and severity prognostic tools for infectious diseases in immunocompromised patients.


Thomas Burke

Manager, Systems Project

Micah Thomas McClain

Associate Professor of Medicine

Geoffrey Steven Ginsburg

Adjunct Professor in the Department of Medicine

Dr. Geoffrey S. Ginsburg's research interests are in the development of novel paradigms for developing and translating genomic information into medical practice and the integration of personalized medicine into health care.


Christopher Wildrick Woods

Wolfgang Joklik Distinguished Professor of Global Health

1. Emerging Infections
2. Global Health
3. Epidemiology of infectious diseases
4. Clinical microbiology and diagnostics
5. Bioterrorism Preparedness
6. Surveillance for communicable diseases
7. Antimicrobial resistance


Ephraim Tsalik

Adjunct Associate Professor in the Department of Medicine

My research at Duke has focused on understanding the dynamic between host and pathogen so as to discover and develop host-response markers that can diagnose and predict health and disease.  This new and evolving approach to diagnosing illness has the potential to significantly impact individual as well as public health considering the rise of antibiotic resistance.

With any potential infectious disease diagnosis, it is difficult, if not impossible, to determine at the time of presentation what the underlying cause of illness is.  For example, acute respiratory illness is among the most frequent reasons for patients to seek care. These symptoms, such as cough, sore throat, and fever may be due to a bacterial infection, viral infection, both, or a non-infectious condition such as asthma or allergies.  Given the difficulties in making the diagnosis, most patients are inappropriately given antibacterials.  However, each of these etiologies (bacteria, virus, or something else entirely) leaves a fingerprint embedded in the host’s response. We are very interested in finding those fingerprints and exploiting them to generate new approaches to understand, diagnose, and manage disease.

These principles also apply to sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Just as with acute respiratory illness, it is often difficult to identify whether infection is responsible for a patient’s critical illness.  We have embarked on a number of research programs that aim to better identify sepsis; define sepsis subtypes that can be used to guide future clinical research; and to better predict sepsis outcomes.  These efforts have focused on many systems biology modalities including transcriptomics, miRNA, metabolomics, and proteomics.  Consequently, our Data Science team has utilized these highly complex data to develop new statistical methods, furthering both the clinical and statistical research communities.

These examples are just a small sampling of the breadth of research Dr. Tsalik and his colleagues have conducted.  

In April 2022, Dr. Tsalik has joined Danaher Diagnostics as the VP and Chief Scientific Officer for Infectious Disease, where he is applying this experience in biomarkers and diagnostics to shape the future of diagnostics in ID. 

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