Using machine learning to predict blood clots in COPD patients

Machine Learning-based Models in Prediction of DVT and PTE in AECOPD Patients: a Multi-institution Study

This study is trying to see if using computer technology can help predict the risk of blood clots in patients with COPD who are having a worsening of their symptoms.

Quick facts

• Study type: Observational
• Enrollment: 1000 (estimated)
• Ages: 18 Years to 90 Years
• Sex: All
• Sponsor: West China Hospital (Academic / other)
• Locations: 1 site (Shenzhen, Guangdong)
• Trial ID: NCT05905874 on ClinicalTrials.gov

What this trial studies

This observational study aims to develop machine learning-based models to predict the risk of deep vein thrombosis (DVT) and pulmonary thromboembolism (PTE) in patients experiencing acute exacerbations of chronic obstructive pulmonary disease (AECOPD). By analyzing data from patients diagnosed with AECOPD who undergo CT pulmonary angiography, the study seeks to identify patterns and risk factors associated with these complications. The approach leverages advanced computational techniques to enhance predictive accuracy and improve patient management strategies.

Who should consider this trial

Good fit: Ideal candidates for this study are patients diagnosed with AECOPD who are undergoing CT pulmonary angiography.

Not a fit: Patients with a history of pulmonary thromboembolism or those receiving anticoagulant treatment prior to enrollment may not benefit from this study.

Why it matters

Potential benefit: If successful, this study could lead to better risk assessment and prevention strategies for blood clots in COPD patients, potentially reducing hospitalizations and improving patient outcomes.

How similar studies have performed: While machine learning approaches in predicting thromboembolic events are emerging, this specific application in AECOPD patients is relatively novel and has not been extensively tested.

Eligibility criteria

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.

Where this trial is running

Shenzhen, Guangdong

• University of Chinese Academy of Sciences Shenzhen Hospital, Shenzhen, People's Republic of China & The first Affiliated Hospital of Jinan University — Shenzhen, Guangdong, China (Recruiting)

Study contacts

• Study coordinator: Tao Wang

Conditions Machine Learning; Chronic Obstructive Pulmonary Disease; Chronic Obstructive Pulmonary Disease With Acute Exacerbation, Unspecified; Pulmonary Thromboembolisms; Deep Vein Thrombosis