Analysis of Exhaled Breath From Patients With Lung-/Airway Diseases (BigExBrESs)

NCT ID: NCT05458934

Last Updated: 2024-08-15

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

5000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-05-11

Study Completion Date

2026-12-31

Brief Summary

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Ananlyis of exhaled breath of patients with lung-/airway diseases to identify and distinguish respiratory diseases, and improve the disease manangament.

Detailed Description

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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.

Conditions

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Lung-/Airway Diseases

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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Lung-/Airway diseases

Patients with lung-/airway diseases

No intervention

Intervention Type OTHER

no intervention

Controls

Participants without lung-/airway disease

No intervention

Intervention Type OTHER

no intervention

Interventions

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No intervention

no intervention

Intervention Type OTHER

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Deep Breath Intelligence (DBI)

UNKNOWN

Sponsor Role collaborator

University of Zurich

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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University Hospital Zurich

Zurich, Canton of Zurich, Switzerland

Site Status RECRUITING

Countries

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Switzerland

Central Contacts

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Malcolm Kohler, MD

Role: CONTACT

+41442551111

Noriane Sievi, MSc

Role: CONTACT

+41442559815

Facility Contacts

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Malcolm Kohler, MD

Role: primary

+41442551111

Noriane Sievi, MSc

Role: backup

+41442559815

References

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Basler S, Fricke K, Sievi NA, Arvaji A, Schmidt F, Herth J, Baur DM, Kohler M, Ulrich S, Lichtblau M. Exploring breath metabolomics as a non-invasive tool for detecting pulmonary vascular disease. Eur Heart J Open. 2025 May 23;5(3):oeaf060. doi: 10.1093/ehjopen/oeaf060. eCollection 2025 May.

Reference Type DERIVED
PMID: 40474893 (View on PubMed)

Other Identifiers

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2021-00539

Identifier Type: -

Identifier Source: org_study_id

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