Clinical trial NCT06528418: Identification of Multiple Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath
Official title: Exploration and Study on the Identification of Various Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Lung Cancer
Lung Infection
COPD
Bronchitis
Pulmonary Fibrosis
Pulmonary Embolism
Pulmonary Arterial Hypertension
Pulmonary Tuberculosis
Pulmonary Abscess
Emphysema
Lung Injury
Cystic Fibrosis of the Lung
Bronchial Asthma
Bronchiectasis
Interstitial Lung Disease
Preserved Ratio Impaired Spirometry
Keywords: Pulmonary Disease
Volatile Organic Compounds
Human Exhaled Breath
micro Gas Chromatography-photoionisation
detector (μGC-PID) system
Lead sponsor: ChromX Health
Sponsor class: INDUSTRY
Collaborators: name: The First Affiliated Hospital of Guangzhou Medical University
name: First People's Hospital of Foshan
name: Sichuan Cancer Hospital and Research Institute
name: Liwan District Central Hospital
name: Shanghai Chest Hospital
name: Peking Union Medical College Hospital
name: Guangzhou Development Zone Hospital
name: Huangpu District Hongshan Street Community Health Service Center
name: Huangpu District Chinese Medicine Hospital
name: Fifth Affiliated Hospital of Guangzhou Medical University
name: Huangpu District Jiufo Street Community Health Service Center
name: Huangpu District Xinlong Town Central Hospital
name: Huangpu District Yonghe Street Community Health Service Center
name: Huangpu District Lianhe Street Second Community Health Service Center
First posted: 2024-07-30
Enrollment: 10000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: Patients with abnormal lung CT images within the past six months, including 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 .

Brief summary: 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:

1. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose several common pulmonary diseases.
2. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose more pulmonary diseases.

Detailed description: This is a prospective, cross-sectional, observational cohort study aimed at recruiting 10,000 participants with multiple pulmonary disease, including 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 . Exhaled breath samples from these participants will be collected and analyzed using Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system. Upon obtaining the μGC-PID results, a comprehensive evaluation of the diagnostic capabilities of exhaled breath samples in differentiating various pulmonary diseases will be performed, leveraging clinical diagnostic results, CT examination data, and clinical data.

Eligibility criteria: 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.

Primary outcomes:
measure: The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases.
timeFrame: 2 years
Secondary outcomes:
measure: The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of more pulmonary diseases.
timeFrame: 2 years
Other outcomes:
measure: Establish an exhaled breath VOC model for predicting specific gene mutations in some lung diseases.
timeFrame: 2 years
Locations:
city: Guangzhou
country: China
facility: The First Affiliated Hospital of Guangzhou Medical University
state: Guangdong
Study officials:
affiliation: The First Affiliated Hospital of Guangzhou Medical University
name: Jianxing He, MD
role: STUDY_CHAIR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.