The hypothesis underlying this work is the identification of different sub phenotypes of patients with infective endocarditis through the study of the host's response to infection. Furthemore, metagenomic sequencing may be a helpful supplement to IE diagnostic, especially when conventional tests fail to yield a diagnosis.
Infective endocarditis is a life-threatening infection of heart valves and adjacent structures characterized by vegetations on valves and other endocardial surfaces, with tissue destruction and risk of embolization. The clinical variability, including the heterogeneous response to infection and the different antibiotic treatments make the identification of the underlying pathogens of infective endocarditis (IE) is critical for precision therapy. Virulence factors mediate tissue adherence, host infiltration, immune resistance/evasion, and dynamic stress responses and confer enhanced pathogen survival, proliferation, and host invasion in animal models of infective endocarditis. To identify the microorganism and simultaneously considered the response of the host to the infection could improve the management of the infective endocarditis.
Study Type
OBSERVATIONAL
Enrollment
32
Hospital Universitario Central de Asturias
Oviedo, Principality of Asturias, Spain
To characterize a cohort of patients diagnosed with infective endocarditis
Clinical, microbiological and echocardiographic description of patients with infective endocarditis.
Time frame: 2 year
Identification of the underlying pathogens of infective endocarditis (IE)
We will employ next-generation sequencing for pathogens and antimicrobial resistance detection in IE patients.
Time frame: 2 year
Host response classification for infective endocarditis (IE)
Differential baseline expression of some genes may indicate resilience to infection.
Time frame: 2 year
Host response predictors of sepsis outcomes
We will develop and use advanced bioinformatic, metabolomic, proteomic and mRNA sequencing technologies to identify specific changes, or biomarkers, in patient blood samples that predict outcome in sepsis.
Time frame: 2 year
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