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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Frequent exacerbation of COPD | |||
| Non-frequent exacerbation of COPD |
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| Measure | Description | Time Frame |
|---|---|---|
| Evaluate the predictive performance of the COPD frequent seizure risk prediction model based on the area under the ROC curve. | According to the Area Under Curve (AUC) of ROC, the largest one has the best predictive performance. When AUC>0.5, the closer it is to 1, the better the predictive performance of the model. When AUC=0.5, it indicates poor model fitting and no potential predictive value. | A year |
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Inclusion Criteria:
Exclusion Criteria:
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A patient with severe chronic obstructive pulmonary disease who visited the Department of Respiratory and Critical Care Medicine, Beijing Chaoyang Hospital, Capital Medical University since January 2023
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Li An | Contact | CHN+13681133265 | bjzy818@sina.com |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Beijing Chaoyang Hospital Affiliated to Capital Medical University | Recruiting | Beijing | Beijing Municipality | 100000 | China |
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