The overall objective of this application is to develop a molecular diagnostic for ventilator-associated pneumonia (VAP) that combines host gene expression profiles, proteomic data, existing biomarkers, and clinical data. Our central hypothesis is that infections trigger stereotyped host-responses that can be detected using host gene-expression profiling. These profiles have been identified in various human infectious diseases including respiratory viral infections1, invasive candidiasis2, and bacteremia3. The rationale that underlies the proposed research is that by identifying a VAP profile, we can improve upon the currently inadequate diagnostic approach to these infections. This will open the door to more timely and appropriate treatment, tracking, and prevention of VAP. The central hypothesis will be tested and, thereby, accomplish the objective of this application by pursuing the following specific aims: 1: Identify a cohort of hospitalized patients at high risk of developing VAP by virtue of intubation. Utilize an existing research infrastructure and extensive experience in building repositories to prospectively define a cohort of well-characterized, critically ill patients. Patients in this cohot are expected to be at high risk of developing a hospital-acquired infection. Relevant groups within this cohort include those with hospital-acquired pneumonia (HAP)/VAP, another healthcare-associated infection (HAI), or no infection. 2: Identify host-gene expression and proteomic profiles that correlate with HAP/VAP. Based on preliminary data, the working hypothesis is that gene expression and proteomic profiles can distinguish infected from non-infected states and can distinguish between different infectious etiologies. The proposed research will also investigate the postulate that such profiles are detectable before peak clinical symptoms, allowing for the possibility of an early diagnostic tool. 3. Build a clinical-molecular classifier to diagnose HAP/VAP. We hypothesize that HAP/VAP diagnosis can be improved by incorporating host gene expression data into conventional clinical diagnostic strategies. The expected outcome of the work proposed in these aims is the development of a HAP/VAP diagnostic derived from both conventional and novel variables. This is expected to have a positive impact, because an improved HAP/VAP diagnostic can fill the current clinical void allowing for improved identification, treatment, and prevention of such infections.
Every year, there are approximately 1.7 million healthcare-associated infections (HAI) leading to nearly 100,000 deaths4. This is especially relevant to the VA Health System, which cares for older and sicker patients than the general population that are more likely to be hospitalized and have longer hospitalizations. In particular, ventilator-associated pneumonia (VAP) is associated with substantial morbidity, mortality, and cost to the healthcare system5. Accurately identifying patients with VAP enables efforts to treat, track, and prevent such infections. Unfortunately, there is no well-validated, adequately reliable diagnostic for VAP6, impeding several areas of clinical practice and research. A better diagnostic strategy can accomplish several goals. It will improve monitoring of VAP events, which in turn informs prevention and infection control strategies. Second, a more sensitive diagnostic can identify patients most likely to benefit from prompt, appropriate, and adequate treatment. Finally, a VAP diagnostic with improved specificity can limit antibiotic overuse.
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|Tsalik, Ephraim L; Henao, Ricardo; Nichols, Marshall et al. (2016) Host gene expression classifiers diagnose acute respiratory illness etiology. Sci Transl Med 8:322ra11|
|Tsalik, Ephraim L; Willig, Laurel K; Rice, Brandon J et al. (2015) Renal systems biology of patients with systemic inflammatory response syndrome. Kidney Int 88:804-14|
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|Tsalik, Ephraim L; Langley, Raymond J; Dinwiddie, Darrell L et al. (2014) An integrated transcriptome and expressed variant analysis of sepsis survival and death. Genome Med 6:111|
|Zaas, Aimee K; Burke, Thomas; Chen, Minhua et al. (2013) A host-based RT-PCR gene expression signature to identify acute respiratory viral infection. Sci Transl Med 5:203ra126|