Lung-/Airway Diseases
Conditions
Brief summary
Ananlyis of exhaled breath of patients with lung-/airway diseases to identify and distinguish respiratory diseases, and improve the disease manangament.
Detailed description
Breath analysis offers a unique opportunity to non-invasively retrieve unlimited samples of relevant information on the ongoing internal biochemical processes, as parts of the most volatile components in blood reach the gas phase and are subsequently exhaled (e.g. detection of ethanol in breath). Analysis of exhaled breath provides biochemical information about the metabolism and the pathophysiological state. Chemical analysis of exhaled breath with mass spectrometry identified numerous volatile organic compounds (VOCs) \[low molecular weightcomponents:\< 500 Dalton (Da)\]. Using Big Data analytics, specific patterns of diverse respiratory disease (e.g. for COPD and asthma) can be identified and the different diseases can be distinguished from each other. Specific Big Data Analytics might also improve the management of disease (e.g.avoiding of exacerbations) by identifying early signs of exacerbation risk.
Interventions
OTHERNo intervention
no intervention
Sponsors
University of Zurich
Deep Breath Intelligence (DBI)
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
Yes
Inclusion criteria
* Signed informed consent * Age ≥ 18 years at study entry
Exclusion criteria
* Physical or intellectual impairment precluding informed consent or protocol adherence * Known pregnancy
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Molecular breath pattern | at baseline | Comparison of molecular weights of breath features from patients with different respiratory diseases |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change in molecular breath pattern | over one year | Change in the molecular breath pattern during the course of the disease (untargeted approach) |
| Symptoms | at baseline | Correlation/Association of molecular weights of specific breath features with symptoms (e.g. dyspnea) |
| Inflammatory markers | at baseline | Correlation/Association of molecular weights of specific breath features with inflammatory markers (e.g. eosinophils) |
| Controls | at baseline | Differentiation of molecular breath pattern (m/z ratios of molecules in exhaled breath) between diseased patients and participants without respiratory disease (untargeted approach) |
Countries
Switzerland
Contacts
CONTACTMalcolm Kohler, MD
CONTACTNoriane Sievi, MSc
Outcome results
None listed