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

Официальное название

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

Обзор

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.

Подробное описание

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.

Вмешательства

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

Первичные конечные точки

• The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases. [Срок оценки: 2 years]

Вторичные конечные точки (1)

• The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of more pulmonary diseases. [Срок оценки: 2 years]

Критерии участия

Критерии включения

• 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.

Критерии исключения

• 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.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Китай · 1 центр

• The First Affiliated Hospital of Guangzhou Medical University — Гуанчжоу

Публикации

• 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. PMID 32526187
• 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. PMID 33374433
• 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. PMID 23259710
• 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. PMID 36690293
• 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. PMID 34415806

Идентификаторы

NCT: NCT06528418 · MLD001

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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