Respiratory microbiome and clinical data analysis for the prediction of acute exacerbations in COPD

COPD is a devastating disease for which no curative treatments are available. Global prevalence of COPD is estimated at 12% and is expected to rise over the next decades, due to increasing smoking behavior in developing countries and aging populations in high-income countries. The clinical course of COPD is characterized by exacerbations, acute worsening of symptoms. Exacerbations incur extreme costs to society due to the need for acute treatment and hospitalisation. Finally, exacerbations play a crucial role in the progression of lung function deterioration. About 10% of patients hospitalized with exacerbation will not survive, while another 15% will not survive beyond 1 year.

Prevention of exacerbations is one of the key aims of COPD treatment but is largely ineffective. Insufficient understanding of the pathobiology and heterogeneity of these events and lack of validated biomarkers to predict and optimize treatment of exacerbations contribute to this tremendous unmet need. The onset of exacerbations was recently shown to coincide with a shift in the respiratory tract microbiota (RTM). Regular monitoring of the RTM in exacerbation prone COPD patients thus represents an opportunity to predict exacerbation occurrence earlier. This enables clinicians to initiate appropriate therapies to prevent exacerbations.

An easy-to-use technique for the analysis of the RTM in daily clinical practice will enable appropriate therapy early on and reduce the risk of life-threatening exacerbations.
REDALERT’s goal is to combine the ISPro technology and geneXplain platform to develop an integrated solution for routine RTM analysis with:
A) novel processing methods for ISPro to accurately characterize the RTM and the relative abundance and shifts therein of microbiota
B) clinical decision-making algorithms based on the geneXplain platform to predict exacerbations from RTM samples and associated clinical patient data
C) integration with two main hospital information systems to include additional patient health data.

A novel approach/algorithm for individualised risk assessment of COPD exacerbations, which integrates both microbiological data and clinical information, improves the accuracy of exacerbation prediction in COPD patients over methods based on clinical parameters alone.

9