Bronchial Asthma, Bronchiectasis, Bronchitis, COPD, Cystic Fibrosis of the Lung, Emphysema, Interstitial Lung Disease, Lung Cancer, Lung Infection, Lung Injury, Preserved Ratio Impaired Spirometry, Pulmonary Abscess, Pulmonary Arterial Hypertension, Pulmonary Embolism, Pulmonary Fibrosis, Pulmonary Tuberculosis
Conditions
Keywords
Pulmonary Disease, Volatile Organic Compounds, Human Exhaled Breath, micro Gas Chromatography-photoionisation, detector (μGC-PID) system
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.
Interventions
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
Sponsors
The First Affiliated Hospital of Guangzhou Medical University
First People's Hospital of Foshan
Sichuan Cancer Hospital and Research Institute
Liwan District Central Hospital
Shanghai Chest Hospital
Peking Union Medical College Hospital
Guangzhou Development Zone Hospital
Huangpu District Hongshan Street Community Health Service Center
Huangpu District Chinese Medicine Hospital
Fifth Affiliated Hospital of Guangzhou Medical University
Huangpu District Jiufo Street Community Health Service Center
Huangpu District Xinlong Town Central Hospital
Huangpu District Yonghe Street Community Health Service Center
Huangpu District Lianhe Street Second Community Health Service Center
ChromX Health
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 100 Years
Healthy volunteers
Yes
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases. | 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). |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of more pulmonary diseases. | 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). |
Other
| Measure | Time frame | Description |
|---|---|---|
| Establish an exhaled breath VOC model for predicting specific gene mutations in some lung diseases. | 2 years | 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 |
Countries
China
Contacts
Primary ContactHengrui Liang, MD
Outcome results
None listed