Lower Respiratory Tract Infection Clinical Trial
This study is Observational multi-center, prospective study to characterize novel pathogen- and host-related factors in hospitalized patients and patients in the Emergency Department (ED) presenting with lower respiratory tract infections (LRTI) and/or sepsis.The Study's objective is to develop a novel multi-parametric diagnostic model for the management of patients with LRTI and/or sepsis that will be based on novel pathogen- and host-related factors.
The TAILORED-Treatment consortium was established to develop new tools aimed to increase the
effectiveness of antibiotic and antifungal therapy, reduce adverse events, and help limit
the emergence of antimicrobial resistance in children and adults.
In reality, targeted antimicrobial therapy can most effectively be achieved by utilizing
personalized data to facilitate a tailored and optimized approach to individual patient
treatment. This can best be achieved by utilizing knowledge gained from both host-centered
and pathogen-centered parameters during health and disease. Unfortunately, these parameters
have traditionally, tended to be measured independently (for example using microbial culture
or PCR-based methods for microbial detection, or measurement of the immune response to
infections and/or blood-based biomarkers in the host), and used on an ad hoc basis without
careful integration for the best treatment of the patient. However, recent advances in the
development of high-throughput and sensitive molecular-based technologies, on-line internet
database access tools, and bioinformatics analysis, now means that the goal of personalized
medicine and treatment is in sight. Unfortunately however, there currently exists a
technological gap between recent state-of-the-art methodologies (for example with respect to
gaining new insights into novel host-pathogen interactions) and laboratory-to-bedside
results to benefit patients, physicians and society as a whole. The TAILORED-Treatment
project is designed to bridge this technological gap in order to generate novel insights and
innovations that are readily exploitable for the maximum benefit of multiple stakeholders in
the field of personalized medicine and infectious diseases.
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Observational Model: Cohort, Time Perspective: Prospective
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