Coccidioidomycosis, caused by the highly infectious dimorphic fungi Coccidioides posadasii and C. immitis, inflicts a heavy and rapidly growing burden of disease, with 150,000 new cases annually in the United States. Clinical symptoms of coccidioidomycosis are highly variable, depending on the degree of exposure and robustness of host cell-mediated immunity ? 40% of exposed individuals develop an acute primary pulmonary coccidioidomycosis syndrome indistinguishable from regular community-acquired pneumonia (CAP). While most of these patients eventually resolve their infection, 41-43% are hospitalized for a median of 6 days, with disabling symptoms lasting several months. A subset of patients, especially individuals with impaired cell-mediated immunity, progress to other, more severe coccidioidal clinical syndromes, including disseminated infection, with high rates of morbidity and death. Although coccidioidomycosis causes 30% of CAP cases in highly endemic regions, the clinical presentation is challenging to differentiate from viral or bacterial CAP and available diagnostic tests have significant limitations, leading to median diagnostic delays of 23-48 days, with many patients having delays lasting several months. Meanwhile, patients seek medical care for their symptoms, with unnecessary hospitalizations, exposure to empiric broad- spectrum antibiotics, laboratory tests, imaging, and invasive procedures. A critical barrier to improving clinical outcomes in these patients is the lack of reliable diagnostic methods that identify coccidioidomycosis early in the course of infection and distinguish it from other common infections with a similar clinical presentation. Once coccidioidomycosis is diagnosed, it is also challenging to determine whether patients are responding to antifungal therapy, due to slow resolution of symptoms and abnormal imaging and laboratory findings. To address this unmet need, we propose a novel approach to the diagnosis of coccidioidomycosis based on detection of fungal volatile metabolites in the breath. We will test the hypotheses that (a) patients with coccidioidomycosis have unique breath metabolites that differentiate them from patients with other infections, and (b) kinetics of these metabolites predict responses to antifungal therapy. We will: (1) identify breath volatile metabolites that distinguish patients with coccidioidomycosis from those with similar clinical syndromes, including CAP and other mycoses, and (2) examine the relationship between early changes in these breath metabolites in patients with coccidioidomycosis treated with antifungal therapy and their clinical outcome. Successful completion of these aims will lay the groundwork for a novel assay for the direct detection of Coccidioides metabolism that can be coupled to a point-of-care gas sensor system for the rapid, bedside identification of patients with coccidioidomycosis, reducing diagnostic delays, guiding appropriate initiation of treatment in patients at risk for severe or disseminated disease, averting unnecessary antibiotic use, and improving clinical outcomes in patients with this morbid, life-threatening infection.
Coccidioidomycosis, or Valley Fever, inflicts a heavy and growing burden of disease in the Americas, with over 150,000 new cases per year in the United States alone. Because symptoms of Valley Fever are nonspecific and currently available diagnostic tests are unable to differentiate Valley Fever from other infections early in the course of infection, patients are often only diagnosed weeks to several months after they initially present with symptoms. We will identify fungal metabolites in the breath that (a) can be used to diagnose Valley Fever earlier in the course of infection than currently possible, differentiating it from other fungal infections and other common infections such as viral or bacterial community-acquired pneumonia, and (b) allow monitoring of the clinical response to antifungal therapy, with the ultimate goal of improving clinical outcomes in patients with this highly debilitating and life-threatening infection.
