Risk Prediction Model for Exacerbating Phenotype in Patients With Chronic Obstructive Pulmonary Disease

ConditionChronic Obstructive Pulmonary Disease Severe

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

Age40-85

SponsorLi An

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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

Disqualifiers

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

Trial population

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

Trial design

Design model

Case-control

Time perspective

Prospective

Treatments tested in this trial

Not listed

Trial groups

365 Participants

are grouped into 2 trial groups

Group A: Frequent exacerbation of COPD

Group B: Non-frequent exacerbation of COPD

Trial outcomes

Primary outcomes

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.

Time frame

A year

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Li An

Lead sponsor

Beijing Chao Yang Hospital

Sponsor institution

Source ClinicalTrials.gov

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