[Venous compression ultrasonography of the lower limbs: a diagnostic tool in the hands of pneumologists]

Residual thrombosis after a first episode of proximal deep venous thrombosis

The aim of this study was to analyse the normalization rate of compression ultrasonography after a first episode of proximal deep venous thrombosis (DVT). Patients underwent compressive ultrasound (C-US) examinations during the 3-6 months following the first proximal DVT episode. Normalization rate of compressive ultrasound (C-US) during the follow-up period was 26.4% [95% confidence interval, 16.3-37.3]. Allelic variation in factor XIII gene (Val34Leu) significantly affected the improvement of popliteal residual thrombi (P = 0.019). We also observed a significant negative correlation between D-dimer levels at 3 months and improvement of popliteal residual thrombi (P = 0.016). There was a significant positive correlation between baseline lumen diameter of the femoral thrombi and IL-8 cytokine (P = 0.015). A significant difference was also found between 1 month-tumor necrosis factor (TNF)-α levels and improvement of residual thrombi (P = 0.047). Our results show that normalization after a standard period of anticoagulation is not frequent and procoagulant and inflammatory biomarkers and also some genetic variations might be related to the resolution of thrombosis.

[Predictive Khorana's model in patients with venous thromboembolic disease and cancer]

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.