Preclinical diagnosis of ventilator associated pneumonia in the PICU
Research type
Research Study
Full title
Pre-clinical diagnosis using integrated microbial and host response signatures to improve outcomes from ventilator associated pneumonia in critically ill children
IRAS ID
333103
Contact name
Nazima Pathan
Contact email
Sponsor organisation
University of Cambridge & Cambridge University Hospitals NHS Foundation Trust
Duration of Study in the UK
2 years, 11 months, 30 days
Research summary
Background: Ventilator-associated pneumonia (VAP) is a lung infection that can develop in children who need help breathing with a machine called a ventilator for more than two days. This infection is quite serious and affects about 10-20% of the 18,000 children admitted to Pediatric Intensive Care Units (PICUs) in the UK each year. VAP can lead to longer hospital stays and increases the risk of death. Diagnosing VAP is very difficult because current tests often don’t give clear results, making it hard for doctors to decide on the best treatment.
Objectives: Our research aims to improve the early detection and treatment of VAP. We plan to:1. Monitor changes in the lung's bacterial community and the body's immune response in children on long-term ventilators to identify early signs of VAP.2. Study how resistance to antibiotics affects the bacteria in the lungs during VAP.3. Use advanced computer techniques to analyze these changes and predict which children will develop VAP.4. Find specific markers in the body and lungs that can signal the early stages of VAP, allowing for quicker and more accurate diagnosis.
Clinical benefits: Our findings could lead to the development of new diagnostic tools that could detect VAP in a more accurate and timely manner, improving outcomes for critically ill children. If we could predict who will develop VAP before it leads to lung deterioration, doctors could start treatment sooner, potentially preventing the infection from becoming severe. Understanding how the bacteria and the body's immune response interact will also help improve the accuracy of diagnosing VAP and the use of antibiotics, ensuring they are given only when truly needed. This will reduce unnecessary antibiotic use and help prevent antibiotic resistance. Ultimately, this research has a clear path to clinical translation - the technologies already exist and by selecting the best diagnostic signals for VAP, we could work with industry to develop and test a diagnostic module which could save lives, shorten hospital stays, and provide better care for critically ill children.REC name
London - Camberwell St Giles Research Ethics Committee
REC reference
24/PR/1253
Date of REC Opinion
21 Oct 2024
REC opinion
Further Information Favourable Opinion