Search

Background: The relationship between cancer and venous thromboembolic disease (VTD) are complex because the activated coagulation factors are not only involved in thrombosis but also in malignant processes, such as angiogenesis and metastasis., Objective: To compare phenotypes of extracellular vesicles (EVs), and levels of D-dimer, soluble P-selectin (sP-selectin) and antigenic tissue factor (TF) between unprovoked VTD patients, who did not develop cancer during one-year follow-up, and those with advanced stage of cancer but not associated with VTD., Methods: A prospective study in which we included 138 unprovoked VTD patients and 67 advanced cancer patients, who did not develop thrombosis. Levels of EVs of different cellular origin (platelet, endothelium and leukocyte), EVs positive for tissue factor (TF) and P-selectin glycoprotein ligand 1 were quantified by flow cytometry. D-dimer, soluble P-selectin (sP-selectin) and antigenic TF were determined by ELISA., Results: TF-positive EVs, D-dimer, and sP-selectin were markedly elevated in unprovoked VTD patients compared to cancer patients without association with thrombosis., Conclusions: Levels of TF-positive EVs, D-dimer and sP-selectin are able to discriminate between unprovoked VTD patients not related to cancer and cancer patients not associated with VTD. These results could lead to the application of EVs as biomarkers of both diseases. Key messages : Circulating EVs, specifically TF-positive EVs, in combination with plasmatic markers of hypercoagulable states, such as D-dimer, sP-selectin and antigen TF, are able to discriminate between cancer patients without thrombosis and patients with unprovoked VTD. Research fields could be opened. Future studies will assess if these biomarkers together serve as predicting thrombotic events in cancer populations.

Background and Objective: The predictive Khorana's model was developed to score the thromboembolic disease risk in cancer patients on chemotherapy and to identify which patients would benefit from thromboprophylaxis. We analized the results and applied the predictive Khorana's model in patients with cancer and who were diagnosed with deep vein thrombosis., Material and Methods: Retrospective analysis of prognostic characteristics of Khorana's model in 122 patients based on a prospective analysis., Results: Seventy-nine percent of the total were in the low and intermediate risk category and 21% had high risk according to the Khorana's predictive model. This model had a sensitivity and prognostic precision of 20.8% (95% confidence interval [95% CI]: 14.6-28.7) and a false negatives proportion of 79.2% (95% CI: 1.3-85.4)., Conclusions: Application of this model in our patients would not be enough as the unique tool to identify cancer patients who should receive tromboprophylaxis. The use of both biomarkers and clinical models seems to be the best cost-effective strategy for this purpose. Future, randomized, prospective, placebo-controlled studies are needed for find better treatment strategies in cancer patients., (Copyright © 2013 Elsevier España, S.L. All rights reserved.)

Our objective was to study agreement between the compression ultrasound images taken in our respiratory medicine department and the duplex ultrasound images obtained by radiologists at our hospital for patients admitted to our ward with suspected diagnoses of venous thromboembolism.Seventy-eight consecutive patients admitted to our respiratory medicine ward suspected of venous thromboembolism were enrolled. Both types of images were available for all patients studied. Agreement was 90% with a Kappa coefficient of 0.81. Agreement between the two techniques was good. Therefore, compression ultrasonography is a technique that can be handled by respiratory medicine specialists for the diagnosis of venous thromboembolism.

The publisher regrets that this article has been temporarily removed. A replacement will appear as soon as possible in which the reason for the removal of the article will be specified, or the article will be reinstated. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy. [ABSTRACT FROM AUTHOR]