Overview of risk assessment models for venous thromboembolism in ambulatory patients with cancer. Issue 191 (July 2020)

• Record Type: Journal Article
• Authors: Gerotziafas, Grigoris T.; Mahé, Isabelle; Lefkou, Eleftheria; AboElnazar, Essam; Abdel-Razeq, Hiqmat; Taher, Ali; Antic, Darko; Elalamy, Ismail; Syrigos, Kostas; Van Dreden, Patrick
• Is Part Of: Thrombosis research. Issue 191(2020)Supplement 1
• Page Start: S50
• Page End: S57
• Publication Date: 2020-07
• Subjects: Risk assessment model -- Venous thromboembolism -- Cancer-associated thrombosis -- Cancer; Thrombosis -- Periodicals; 616.135
• DOI: 10.1016/S0049-3848(20)30397-2
• Languages: English
• ISSNs: 0049-3848

Abstract

Abstract : A B S T R A C T Important progress has been made in the development of risk assessment models (RAM) for the identification of outpatients on anticancer treatment at risk of venous thromboembolism (VTE). Since the breakthrough publication of the original Khorana risk score (KRS) more than 10 years ago, a new generation of KRS-based scores have been developed, including the Vienna Cancer and Thrombosis Study, PROTECHT, CONKO, ONCOTEV, TicOnco and the CATS/MICA score. Among these the CATS/MICA score showed that a simplified score composed of only two calibrated predictors, the type of cancer and the D-dimer levels, offers a user-friendly tool for the evaluation of cancer-associated thrombosis (CAT) risk. The COMPASS-CAT score is the first that introduced a more synthetic approach of risk evaluation by combining cancer-related predictors with patient comorbidity in a score which is designed for the types of cancer frequently seen in the community (i.e. breast, lung colon or ovarian cancers) and has been externally validated in independent studies. The Throly score is registered as part of the same group as it has a similar structure to the COMPASS-CAT score and is applicable in patients with lymphoma. The incorporation of specific biomarkers of hypercoagulability to the RAM for CAT offers the possibility to perform a precision medicine approach in the prevention of CAT. The improvement of RAM for CAT with artificial intelligence methodologies and deep learning techniques is the challenge in the near future.