Imperial College London

Academic Institute

Matthieu Komorowski

Unknown

No

NIBDAPC_2024_0040

Acute lung injury (ALI) and its severe form, acute respiratory distress syndrome (ARDS), are life-threatening conditions where the lungs are damaged, reducing their ability to absorb oxygen. ARDS can occur due to lung issues like infections or inflammation from illnesses elsewhere in the body. Before COVID-19, ARDS accounted for about 10% of ICU cases. This increased significantly during the pandemic, as ARDS was a common cause of critical illness and death in COVID-19 patients. In the UK, approximately 20,000 people develop ARDS yearly, with nearly half not surviving. Treatment often involves ventilators and placing patients on their front (proning). However, outside COVID-19, no medications have proven effective for ARDS despite years of research. One reason for this difficulty is that ARDS encompasses different diseases grouped together. Treatments that help one subgroup may not work for another. COVID-19 research has shown that focusing on a specific cause of ARDS can lead to new treatments. My research aims to identify subgroups within ALI/ARDS using clinical data and in future chest X-rays routinely collected in ICUs. Machine learning will analyze patterns from thousands of patient variables to group patients based on their condition's progression over time, called trajectories. Combining X-ray and clinical data is a novel approach, building on earlier studies using complex lab tests. Once subgroups are identified, I’ll evaluate if they align with medical understanding and compare them to previous biological data findings. For example, a subgroup may represent a group of patients who become increasingly difficult to provide support on the ventilator as their lungs become more stiff. We would want to understand if these are consistent with what doctors observe. The goal is to determine if certain treatments work better for specific subgroups, an example of subgroup would be patients who are considered higher inflammation based on fever and blood tests. A group with inflammation-driven ARDS benefit from anti-inflammatory medicines such as steroids. . In a less inflamed subgroup steroids may be harmful as they can increase risk of infection. This approach minimizes harm from non-targeted treatments and advances personalized medicine.

This research benefits the public by advancing personalized medicine for acute respiratory distress syndrome (ARDS), a critical condition affecting thousands each year. By identifying specific patient subgroups and tailoring treatments to their unique needs, it can improve survival rates, reduce harm from ineffective therapies, and optimize healthcare resources. The approach uses existing ICU data and innovative machine learning methods, making it cost-effective and widely applicable. Ultimately, this work paves the way for more precise, effective care, improving outcomes for critically ill patients and their families. I have discussed this research extensively with my PPIE panel during the preparation of my MRC application and in advisory panel meetings since. The PPIE panel members agreed that in addition to the standard metrics such as improved survival from ARDS, shorter length of stay, less time on a ventilator and fewer complications of ICU would be important. Further they indicated that being able to describe a an individuals likely clinical trajectory would be helpful in their expectations and understanding of what would happen to them or their family member.

Public Health Research

02/01/2025

Not applicable

Not applicable

One-off

TRE