- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05905874
Machine Learning-based Models in Prediction of DVT and PTE in AECOPD Patients
Machine Learning-based Models in Prediction of DVT and PTE in AECOPD Patients: a Multi-institution Study
Chronic Obstructive Pulmonary Disease (COPD) is a common respiratory system disease characterized by persistent respiratory symptoms and irreversible airflow restriction, which seriously endangers people's health. Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) refers to individuals who experience continuous deterioration beyond their daily condition and need to change their routine medication. AECOPD is usually caused by viruses and bacteria, and patients require hospitalization, which brings a huge economic burden to society. AECOPD patients often have limited activities. Because long-term chronic hypoxia causes venous blood stasis, siltation causes secondary red blood cell increase, and blood hypercoagulability, AECOPD patients have a high risk of pulmonary embolism (PE).
Pulmonary Thrombo Embolism (PTE) refers to a disease caused by blockage of the pulmonary artery or its branches caused by a thrombus from the venous system or right heart. AECOPD patients experience elevated hemoglobin levels and increased blood viscosity due to long-term hypoxia. At the same time, such patients have decreased activity, venous congestion, and are prone to thrombosis. After the thrombus falls off, it can travel up the vein, causing PTE to occur in the right heart PTE is often secondary to low deep vein thrombosis (DVT). About 70% of patients were diagnosed as deep vein thrombosis in lower limb color ultrasound examination. SteinPD conducted a survey on COPD patients and general patients from multiple hospitals. The results showed that by comparing adult COPD patients with non COPD patients, the relative risk of DVT was 1.30, providing evidence for AECOPD being more likely to combine with PTE AECOPD patients with PTE have similarities in their clinical manifestations. It is difficult to distinguish between the two based solely on symptoms, such as cough, increased sputum production, increased shortness of breath, and difficulty breathing. They lack specificity and are difficult to distinguish between the two based solely on symptoms, which can easily lead to missed diagnosis. CT pulmonary angiography (CTPA) is the gold standard for the diagnosis of PTE, but due to the high cost of testing and high equipment prices, its popularity in grassroots hospitals is not high. Therefore, analyzing the risk factors of AECOPD patients complicated with PTE is of great significance for early identification of PTE. At present, although there are reports on the risk factors for concurrent PTE in AECOPD patients, there is no specific predictive model for predicting PTE in AECOPD patients. In clinical practice, risk assessment tools such as the Caprini risk assessment model and the modified Geneva scale are commonly used for VTE, while the Wells score is the PTE diagnostic likelihood score. The evaluation indicators of these tools are mostly clinical symptoms, and laboratory indicators are less involved, It is difficult to comprehensively reflect the patient's condition, so the specificity of AECOPD patients with PTE is not strong.
The column chart model established in this study presents a visual prediction model, which is convenient for clinical use and has positive help for the early detection of AECOPD patients with PTE. In addition, medical staff can present the calculation results of the column chart model to patients, making it easier for patients to understand. It helps improve the early identification and treatment of AECOPD combined with PTE patients, thereby improving prognosis.
Study Overview
Status
Conditions
Intervention / Treatment
Study Type
Enrollment (Estimated)
Contacts and Locations
Study Contact
- Name: Tao Wang
- Phone Number: +8613456902508
- Email: wt20dy@163.com
Study Contact Backup
- Name: Qinrao Jia
- Phone Number: +8613258389788
- Email: 4941291@qq.com
Study Locations
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-
Guangdong
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Shenzhen, Guangdong, China
- Recruiting
- University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen, People's Republic of China & The first Affiliated Hospital of Jinan University
-
Contact:
- Tao Wang
- Phone Number: 8613258389788
- Email: wt20dy@163.com
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Diagnosis in accordance with AECOPD;
- Perform CT pulmonary angiography examination in present institutions;
- The relevant information to be analyzed is complete.
Exclusion Criteria:
- Patients who already had PTE before the diagnosis of AECOPD;
- Patients with concomitant bronchial asthma, interstitial lung disease, and other lung diseases;
- Patients with other thrombotic related diseases;
- Those who received anticoagulant treatment before enrollment.
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
---|---|
AECOPD patients present with DVT and/or PTE
|
The machine learning-based prediction model will be used to forecast whether the presence of DVT and/or PTE or not in AECOPD patients after standardized treatment.
|
AECOPD patients absent with DVT and/or PTE
|
The machine learning-based prediction model will be used to forecast whether the presence of DVT and/or PTE or not in AECOPD patients after standardized treatment.
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Number of patients present with DVT and/or PTE
Time Frame: 1 year
|
Number of patients present with DVT and/or PTE
|
1 year
|
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
In-hospital mortality
Time Frame: 1 year
|
The occurrence of death due to AECOPD or DVT/PTE
|
1 year
|
ICU admission
Time Frame: 1 year
|
Patients admitted to ICU due to AECOPD severity
|
1 year
|
Collaborators and Investigators
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Estimated)
Study Completion (Estimated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Pathologic Processes
- Cardiovascular Diseases
- Vascular Diseases
- Respiratory Tract Diseases
- Disease Attributes
- Embolism and Thrombosis
- Embolism
- Chronic Disease
- Lung Diseases
- Lung Diseases, Obstructive
- Pulmonary Disease, Chronic Obstructive
- Thrombosis
- Venous Thrombosis
- Thromboembolism
- Pulmonary Embolism
Other Study ID Numbers
- LL-KT-2023018
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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