Identification of Multiple Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath
Exploration and Study on the Identification of Various Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath
The goal of this observational study is to develop an advanced expiratory algorithm model utilizing exhaled breath volatile organic compound (VOC) marker molecules. This model aims to accurately diagnose mutiple pulmonary diseases. The primary objectives it strives to accomplish are:
- To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose several common pulmonary diseases.
- To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose more pulmonary diseases.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Hengrui Liang, MD
- Phone Number: +86 15625064712
- Email: hengrui_liang@163.com
Study Locations
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Guangdong
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Guangzhou, Guangdong, China, 510140
- Recruiting
- The First Affiliated Hospital of Guangzhou Medical University
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Contact:
- Hengrui Liang, MD
- Phone Number: +86 15625064712
- Email: hengrui_liang@163.com
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Males or females, age must be 18 years old or above.
- Patients must meet the CT imaging diagnostic criteria for different lung diseases, and patients must be able to provide electronic versions of CT image data.
- Patients must have a clear clinical diagnosis.
- All participants must sign a written informed consent form.
Exclusion Criteria:
- Pregnant women.
- Individuals with a history of cancer other than lung disease.
- Individuals who have undergone organ transplants or non-autologous (allogeneic) bone marrow or stem cell transplants.
- Individuals with other severe organic diseases or mental illnesses.
- Individuals with metabolic diseases such as diabetes, hyperlipidemia, etc.
- Any other condition that researchers deem unsuitable for participation in this clinical trial.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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pulmonary disease
Individuals with abnormalities in lung CT imaging and clinically diagnosed with lung cancer, lung infection, chronic obstructive pulmonary disease (COPD), bronchitis, pulmonary fibrosis, pulmonary embolism, pulmonary arterial hypertension, tuberculosis, lung abscess, emphysema, radioactive lung injury, cystic fibrosis of the lung, Bronchial Asthma, Bronchiectasis, interstitial lung disease (ILD), preserved ratio impaired spirometry (PRISm) etc .
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Exhaled breath samples from these participants will be collected and analyzed to detect volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID
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normal individual
Individuals with no abnormalities detected in lung CT imaging.
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Exhaled breath samples from these participants will be collected and analyzed to detect volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases.
Time Frame: 2 years
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The diagnostic performance of the exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model will be compared with clinical diagnosis and CT/LDCT diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
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2 years
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of more pulmonary diseases.
Time Frame: 2 years
|
The diagnostic performance of the exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model will be compared with clinical diagnosis and CT/LDCT diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).
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2 years
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Other Outcome Measures
Other Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Establish an exhaled breath VOC model for predicting specific gene mutations in some lung diseases.
Time Frame: 2 years
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Establish an exhaled breath VOC model for predicting specific gene mutations in some lung diseases.
And evaluate the prediction accuracy by comparing the results of specific gene testing
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2 years
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Investigators
Investigators
- Study Chair: Jianxing He, MD, The First Affiliated Hospital of Guangzhou Medical University
Publications and helpful links
General Publications
- GBD Chronic Respiratory Disease Collaborators. Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020 Jun;8(6):585-596. doi: 10.1016/S2213-2600(20)30105-3.
- Ratiu IA, Ligor T, Bocos-Bintintan V, Mayhew CA, Buszewski B. Volatile Organic Compounds in Exhaled Breath as Fingerprints of Lung Cancer, Asthma and COPD. J Clin Med. 2020 Dec 24;10(1):32. doi: 10.3390/jcm10010032.
- van de Kant KD, van der Sande LJ, Jobsis Q, van Schayck OC, Dompeling E. Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review. Respir Res. 2012 Dec 21;13(1):117. doi: 10.1186/1465-9921-13-117.
- Wang J, Janson C, Gislason T, Gunnbjornsdottir M, Jogi R, Orru H, Norback D. Volatile organic compounds (VOC) in homes associated with asthma and lung function among adults in Northern Europe. Environ Pollut. 2023 Mar 15;321:121103. doi: 10.1016/j.envpol.2023.121103. Epub 2023 Jan 21.
- V A B, Subramoniam M, Mathew L. Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose. Expert Rev Mol Diagn. 2021 Nov;21(11):1223-1233. doi: 10.1080/14737159.2021.1971079. Epub 2021 Aug 27.
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
- Vascular Diseases
- Cardiovascular Diseases
- Wounds and Injuries
- Pathologic Processes
- Genetic Diseases, Inborn
- Immune System Diseases
- Respiratory Tract Infections
- Infections
- Respiratory Tract Diseases
- Digestive System Diseases
- Infant, Newborn, Diseases
- Bronchial Diseases
- Lung Diseases, Obstructive
- Respiratory Hypersensitivity
- Hypersensitivity, Immediate
- Hypersensitivity
- Pancreatic Diseases
- Embolism and Thrombosis
- Gram-Positive Bacterial Infections
- Bacterial Infections
- Bacterial Infections and Mycoses
- Suppuration
- Thoracic Injuries
- Actinomycetales Infections
- Mycobacterium Infections
- Hypertension, Pulmonary
- Abscess
- Pulmonary Arterial Hypertension
- Lung Diseases
- Asthma
- Pulmonary Embolism
- Pulmonary Fibrosis
- Fibrosis
- Embolism
- Lung Diseases, Interstitial
- Emphysema
- Cystic Fibrosis
- Lung Injury
- Tuberculosis
- Tuberculosis, Pulmonary
- Bronchiectasis
- Lung Abscess
- Bronchitis
Other Study ID Numbers
Other Study ID Numbers
- MLD001
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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