Machine Learning-based Models in Prediction of DVT and PTE in AECOPD Patients

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-01-01

Study Completion Date

2024-12-31

Brief Summary

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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.

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Detailed Description

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Conditions

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

Study Design

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Observational Model Type

COHORT

Study Time Perspective

OTHER

Study Groups

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AECOPD patients present with DVT and/or PTE

Machine learning-based prediction model

Intervention Type OTHER

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

Machine learning-based prediction model

Intervention Type OTHER

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.

Interventions

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Machine learning-based prediction model

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.

Intervention Type OTHER

Eligibility Criteria

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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.

Minimum Eligible Age

18 Years

Maximum Eligible Age

90 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No