Predicting frequent flare-ups in people with severe COPD using gut bacteria
Builds a model to predict frequent flare-ups in adults with severe COPD, using gut microbiota.
Plain-language title generated from the registry record
Registry title: Risk Prediction Model for Exacerbating Phenotype in Patients With Chronic Obstructive Pulmonary Disease
Key facts
- Where
- Beijing, ChinaSee the site
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 40 and 85 years old?
Source: NCT06198309, who can take part
Where in China
- Beijing Chaoyang Hospital affiliated to Capital Medical UniversityBeijing, ChinaTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Li An
- Trial code
- NCT06198309
- Record updated
- 10 January 2024
- Original description
This study is planned to be conducted based on the cohort of patients with severe chronic obstructive pulmonary disease in our hospital. Based on gut microbiota, random forest was used to search for potential diagnostic biomarkers in patients with frequent acute exacerbation and controls with non frequent acute exacerbation; Construct a frequent acute exacerbation risk prediction model using random forest, support vector machine, and BP neural network models. The development of this study will provide valuable references for the clinical classification and prognosis evaluation of chronic obstructive pulmonary disease (COPD), and improve the health level of COPD patients by further searching for treatable targets.Read full original descriptionShow less
A Risk-predictive Model for Frequent Acute Exacerbation Phenotype in Patients With Severe Chronic Obstructive Pulmonary Disease
This study is planned to be conducted based on the cohort of patients with severe chronic obstructive pulmonary disease in our hospital. Based on gut microbiota, random forest was used to search for potential diagnostic biomarkers in patients with frequent acute exacerbation and controls with non frequent acute exacerbation; Construct a frequent acute exacerbation risk prediction model using random forest, support vector machine, and BP neural network models. The development of this study will provide valuable references for the clinical classification and prognosis evaluation of chronic obstructive pulmonary disease (COPD), and improve the health level of COPD patients by further searching for treatable targets.
Inclusion: Patients who meet the diagnostic criteria for COPD of the global initiative for chronic obstructive lung diseases (GOLD 2022) and GOLD grading Ⅲ - Ⅳ (FEV1/FVC\<70%, FEV1% predicted value ≤ 50% after Bronchiectasis); Age\>40 years old; COPD stable for more than 4 weeks; Short acting Bronchiectasis was not used within 24 hours before this experiment, long acting Bronchiectasis was not used within 48 hours, and glucocorticoids were not used throughout the body in the past month; Patient informed and signed consent form.
Exclusion: Asthma, active pulmonary tuberculosis, interstitial pneumonia and severe Bronchiectasis; Complicated with serious diseases (acute infection, diabetes, stroke, heart disease, liver and kidney dysfunction, cancer or autoimmune disease); History of chronic diarrhea or constipation; History of Gastrointestinal Surgery; No history of using oral hormones or traditional Chinese medicine in the past three months; Using probiotics or antibiotics within the past 4 weeks; Pregnancy or lactation.