Robin Liang
PhD student at TREC
Cancer patients have a high risk of developing venous thromboembolism (VTE), especially in the first 6 months after diagnosis. The risk of VTE can be greatly reduced using anticoagulants to prevent thrombosis, but this treatment brings a high risk of bleeding. This makes it difficult to determine whose risk of thrombosis is greater than the risk of bleeding. Though there are several models that are used in the clinics to predict which cancer patients are likely to benefit from anticoagulation, their accuracy has been disputed. Ingrid Pabinger and colleagues set out to create and validate a clinical prediction model to predict VTE over 6 months in cancer patients.
The new model is simple and includes only two variables: The location of the tumor as a clinical variable and D-dimer concentration as a laboratory measurement. These variables are among many used in previous clinical models and were chosen due to their predictive power and availability in the hospital setting. The researchers used data from two large prospective cohort studies, the Vienna Cancer and Thrombosis Study and the Multinational Cohort Study to Identify Cancer Patients at High Risk of Venous Thromboembolism, to develop and externally validate their new prediction model.
This simple model proved effective in predicting which cancer patients had a low or high risk of VTE during the 6-month follow-up period. It is noteworthy that compared to the most widely used clinical model currently used, it correctly reclassified 31% of VTE patients according to whether or not they did or did not develop a VTE. The new model can help to identify which high-risk patients would benefit from preventative therapy with anticoagulants, thus lowering risk of VTE and minimizing bleeding risk, but more studies are needed to confirm their suggested thresholds.
Reference: Pabinger I. et al. A clinical prediction model for cancer-associated venous thromboembolism: a development and validation study in two independent prospective cohorts. Lancet Haemotol 2018.
