Diagnostic Value of the Padua Score Combined with Thrombotic Biomarker Tissue Plasminogen Activator Inhibitor-1 (tPAI-1) Detection for the Risk of Deep Vein Thrombosis in Patients with Pulmonary Heart Disease
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Keywords

Padua prediction score
Tissue plasminogen activator inhibitor-1 (tPAI-1) detection
Deep vein thrombosis (DVT)
Pulmonary heart disease (cor pulmonale)
Diagnostic accuracy

DOI

10.26689/jcnr.v8i8.8178

Submitted : 2024-08-05
Accepted : 2024-08-20
Published : 2024-09-04

Abstract

This study explores the diagnostic value of combining the Padua score with the thrombotic biomarker tissue plasminogen activator inhibitor-1 (tPAI-1) for assessing the risk of deep vein thrombosis (DVT) in patients with pulmonary heart disease. These patients often exhibit symptoms similar to venous thrombosis, such as dyspnea and bilateral lower limb swelling, complicating differential diagnosis. The Padua Prediction Score assesses the risk of venous thromboembolism (VTE) in hospitalized patients, while tPAI-1, a key fibrinolytic system inhibitor, indicates a hypercoagulable state. Clinical data from hospitalized patients with cor pulmonale were retrospectively analyzed. ROC curves compared the diagnostic value of the Padua score, tPAI-1 levels, and their combined model for predicting DVT risk. Results showed that tPAI-1 levels were significantly higher in DVT patients compared to non-DVT patients. The Padua score demonstrated a sensitivity of 82.61% and a specificity of 55.26% at a cutoff value of 3. The combined model had a significantly higher AUC than the Padua score alone, indicating better discriminatory ability in diagnosing DVT risk. The combination of the Padua score and tPAI-1 detection significantly improves the accuracy of diagnosing DVT risk in patients with pulmonary heart disease, reducing missed and incorrect diagnoses. This study provides a comprehensive assessment tool for clinicians, enhancing the diagnosis and treatment of patients with cor pulmonale complicated by DVT. Future research should validate these findings in larger samples and explore additional thrombotic biomarkers to optimize the predictive model.

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