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NCT Number: NCT06528418

Identification of Multiple 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:

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.

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Key information

Age range

18 year–100 year

Sex eligibility

All sexes

Study type

Observational

Primary location

The First Affiliated Hospital of Guangzhou Medical University

Guangzhou, Guangdong, 510140, China

Location status: Recruiting

Location contact

Hengrui Liang, MD

CONTACT

[email protected]

+86 15625064712

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

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

Other

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

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.

    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).

Secondary outcomes

  1. 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).

Other outcomes

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

    Time frame: 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

Study contacts

Contact information is provided by the study sponsor or research team.

Hengrui Liang, MD

CONTACT

[email protected]

+86 15625064712

Sponsors and collaborators

Lead sponsor

ChromX Health

Industry

Collaborators

  • Fifth Affiliated Hospital of Guangzhou Medical University
  • First People's Hospital of Foshan
  • Guangzhou Development Zone Hospital
  • Huangpu District Chinese Medicine Hospital
  • Huangpu District Hongshan Street Community Health Service Center
  • Huangpu District Jiufo Street Community Health Service Center
  • Huangpu District Lianhe Street Second Community Health Service Center
  • Huangpu District Xinlong Town Central Hospital
  • Huangpu District Yonghe Street Community Health Service Center
  • Liwan District Central Hospital
  • Peking Union Medical College Hospital
  • Shanghai Chest Hospital
  • Sichuan Cancer Hospital and Research Institute
  • The First Affiliated Hospital of Guangzhou Medical University

Registry information

Official study title

Exploration and Study on the Identification of Various Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath

Important dates

Study start
2024
Primary completion
2026
Study completion
2027
First posted
Jul 30, 2024
Registry last updated
Mar 26, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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