Risk Prediction Model for Exacerbating Phenotype in Patients With 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.

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Key information

Conditions

Chronic Obstructive Pulmonary Disease Severe

Age range

40 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Beijing Chaoyang Hospital Affiliated to Capital Medical University

Beijing, Beijing Municipality, 100000, China

Location status: Recruiting

Location contact

Li An, doctorate

CONTACT

[email protected]

CHN+13681133265

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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 criteria

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
Using probiotics or antibiotics within the past 4 weeks
No history of using oral hormones or traditional Chinese medicine in the past three months
Pregnancy or lactation

Treatment and study plan

Primary outcomes

Evaluate the predictive performance of the COPD frequent seizure risk prediction model based on the area under the ROC curve.

Time frame: A year

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.

Study contacts

Contact information is provided by the study sponsor or research team.

Li An

CONTACT

[email protected]

CHN+13681133265

Sponsors and collaborators

Lead sponsor

Li An

Other

Registry information

Official study title

A Risk-predictive Model for Frequent Acute Exacerbation Phenotype in Patients With Severe Chronic Obstructive Pulmonary Disease

Important dates

Study start: 2023
Primary completion: 2027
Study completion: 2027
First posted: Jan 10, 2024
Registry last updated: Jan 10, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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