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Risk Prediction Model for Exacerbating Phenotype in Patients With Chronic Obstructive Pulmonary Disease
Sponsor: Li An
Summary
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.
Official title: A Risk-predictive Model for Frequent Acute Exacerbation Phenotype in Patients With Severe Chronic Obstructive Pulmonary Disease
Key Details
Gender
All
Age Range
40 Years - 85 Years
Study Type
OBSERVATIONAL
Enrollment
365
Start Date
2023-05-01
Completion Date
2027-12-01
Last Updated
2024-01-10
Healthy Volunteers
Not specified
Locations (1)
Beijing Chaoyang Hospital Affiliated to Capital Medical University
Beijing, Beijing Municipality, China