Health TrialsbyHealthPanel

Identification of Multiple Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath

ConditionsLung CancerLung InfectionCOPDBronchitisPulmonary FibrosisPulmonary EmbolismPulmonary Arterial HypertensionPulmonary TuberculosisPulmonary AbscessEmphysemaLung InjuryCystic Fibrosis of the LungBronchial AsthmaBronchiectasisInterstitial Lung DiseasePreserved Ratio Impaired Spirometry
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18-100
SponsorChromX Health

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

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.

Disqualifiers

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.

Trial 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 .

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system

    Other intervention

    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

Treatment groups

10,000 Participants
are divided into 2 treatment groups
Group A: pulmonary disease1 intervention
Group B: normal individual1 intervention

Trial outcomes

Primary outcomes

1

The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases.

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).

Time frame
2 years

Secondary outcomes

1

The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of more pulmonary diseases.

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).

Time frame
2 years

Other outcomes

1

Establish an exhaled breath VOC model for predicting specific gene mutations in some lung diseases.

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

Time frame
2 years

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

ChromX Health

Lead sponsor

The First Affiliated Hospital of Guangzhou Medical University

Collaborator

First People's Hospital of Foshan

Collaborator

Sichuan Cancer Hospital and Research Institute

Collaborator

Liwan District Central Hospital

Collaborator

Shanghai Chest Hospital

Collaborator

Peking Union Medical College Hospital

Collaborator

Guangzhou Development Zone Hospital

Collaborator

Huangpu District Hongshan Street Community Health Service Center

Collaborator

Huangpu District Chinese Medicine Hospital

Collaborator

Fifth Affiliated Hospital of Guangzhou Medical University

Collaborator

Huangpu District Jiufo Street Community Health Service Center

Collaborator

Huangpu District Xinlong Town Central Hospital

Collaborator

Huangpu District Yonghe Street Community Health Service Center

Collaborator

Huangpu District Lianhe Street Second Community Health Service Center

Collaborator

Source ClinicalTrials.gov
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