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Thank you for attending THSNA 2026. The virtual meeting is now closed.
Presentation Details
| Comparative Analysis of Cancer-Induced Venous Thromboembolism Predicators and Risk Assessment Models: A Systematic Review Tarek A.Issa1, Noor A.Haykal1, Yasmin M.Elsayed1, Islam A.Eljilany2, Hazem F.Elewa1. 1College of Pharmacy, QU Health, Qatar University, Doha, Qatar.2H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA |
Abstract
Background: Cancer-associated thromboembolism (CAT) is a major cause of morbidity and mortality in cancer patients. Several Risk Assessment Models (RAMs), such as the Khorana, Vienna CATS, and COMPASS-CAT scores, have been developed to categorize patients by their risk of venous thromboembolism (VTE). However, their predictive accuracy differs across cancer types and patient populations. Given the increasing incidence of CAT across different cancer populations, evaluating clinical and biomarker-driven risk assessment models (RAMs) is critical for guiding thromboprophylaxis. Objective: This systematic review aims to identify, compare, and evaluate the efficacy, predictability, validity, and clinical value of VTE risk assessment models in cancer patients. The aim is to summarize reliable clinical or biomarker-derived tools used to guide thromboprophylaxis, and predictors that consistently demonstrate strong associations with VTE risk across cancer types. Methods: Searches were conducted in PubMed, EMBASE, and Scopus, with supplementary searches via Google Scholar. Primary studies in English that developed and validated new RAMs for cancer patients at risk of VTE up to May 2025 were included. Three independent authors screened, selected, extracted, and qualitatively synthesized the data. In total, 28 studies published between 2017 and 2025 were included, covering more than 15,000 cancer patients across China, the United States of America, Australia, and Europe. Both inpatient and outpatient settings were represented. Most studies were retrospective cohorts, though recent years have demonstrated an increase in prospective validation. Results: A total of 2,022 articles were initially identified, and 28 studies met the inclusion criteria. Logistic regression-based nomograms demonstrated strong performance, especially in Chinese cohorts (AUC 0.854–0.929). Machine learning (ML) models, such as XGBoost and random forest, achieved the highest AUC values (0.745–0.990), consistently outperforming Khorana (AUC 0.756–0.875) and Caprini (0.589–0.759). D-dimer, present in over 80% of models, was the most reliable and consistent predictor (OR = 24.3 in colorectal cancer; OR = 5.58 in lung cancer; HR = 1.14 in ovarian cancer). Other predictors included central venous catheter (OR =7.7), advanced cancer stage (OR = 2.7), BMI (OR =2.0), and chemotherapy (OR = 1.66). Additional tumor-specific predictors, such as Ki67 in ovarian cancer and CEA in lung cancer, were also identified. Validation practices differed across regions, with 85.7% of European studies performing external validation compared to 42.9% in Asia. Conclusion: Models combining clinical predictors with biomarkers and validated externally showed the greatest clinical potential. D-dimer was the strongest and most consistent predictor across cancer types. Machine learning models provided superior discrimination but require further external validation to ensure real-world applicability.
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No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.