Enhancing Venous Thromboembolism Risk Prediction through Novel Biomarkers and Inflammatory Indicators

Abstract BACKGROUND: Venous thromboembolism (VTE), including deep venous thrombosis (DVT) and pulmonary embolism, is a common disease and considerable cause of morbidity and mortality worldwide, especially among hospitalized or postoperative patients. However, the prediction accuracy calculated with the help of traditional methods (including Wells score and D-dimer tests) is not so ideal to predict this risk of disease; therefore, novel biomarkers need to be explored. This study aims to evaluate the potential of new biomarkers, such as thrombin generation, microRNAs (miRNAs ) , Factor VIII, and inflammatory markers such as C-reactive protein (CRP) and interleukin-6 (IL-6), to enhance the prediction of VTE risk, thereby facilitating earlier and more precise interventions. SUBJECTS AND METHODS: A case–control study in care hospital enrolled 500 participants, including 250 with confirmed VTE and 250 controls without complications. Participants aged 18 years or older who had not received anticoagulant treatment before the start of the trial exclusion criterion included current infection, pregnancy, and hematological disorders. Plasma was collected for biomarker analysis in the following: D-dimer, thrombin generation, factor VIII levels, and inflammatory markers using enzyme-linked immunosorbent assay (ELISA) (CRP/IL-6/miRNAs), CAT, and real-time polymerase chain reaction. RESULTS: The current study revealed that D-dimer levels were much higher in VTE patients compared with controls while Thrombin generation time decreased in VTE patients, miRNAs miR-145 and miR-223 are highly expressed in those patients. Factor VIII and inflammatory markers were significantly elevated in patients compared with the control group. CONCLUSION: The study highlights the importance of novel biomarkers such as thrombin generation time, factor VIII levels, circulating miRNAs, and inflammatory markers in assessing VTE risk. These biomarkers may be able to identify people at a greater risk, which could lead to more personalized medical interventions for high-risk populations. The finding underlines the importance of integrating these biomarkers into clinical guidelines for risk prediction and prevention compared to patients without these conditions.