Sharma Swati, Sahni Sumit, Antoniak Silvio
UNC Blood Research Center, Department of Pathology and Laboratory Medicine, UNC School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
School of Open Learning, University of Delhi, Delhi, India.
Curr Opin Hematol. 2025 May 1;32(3):168-175. doi: 10.1097/MOH.0000000000000862. Epub 2025 Feb 7.
To explore the use of large datasets in predicting and managing cancer-associated venous thromboembolism (CAT) by stratifying patients into risk groups. This includes evaluating current predictive models and identifying potential improvements to enhance clinical decision-making.
Cancer patients are at an elevated risk of developing venous thromboembolism (VTE), which significantly impacts mortality and quality of life. Traditional approaches to risk assessment fail to account for the procoagulant changes associated with cancer, making individualized risk prediction a challenge. Current clinical guidelines as per ASCO recommend risk assessment before chemotherapy and endorse thromboprophylaxis as a standard preventive measure. Since any cancer population is highly heterogeneous in terms of VTE risk, predicting the risk of CAT is an oncological challenge. To address this, different predictive models have been developed to stratify patients by risk, enabling targeted thromboprophylaxis. However, these models vary in accuracy and utility. The present review discusses the pros and cons of these different models.
The review examines existing CAT risk prediction models, highlighting their strengths, limitations, and diagnostic performance. It also identifies additional variables that could enhance these models to improve their effectiveness in guiding clinicians toward better risk stratification and treatment decisions for cancer patients.
探讨通过将患者分层为风险组，利用大型数据集预测和管理癌症相关静脉血栓栓塞（CAT）。这包括评估当前的预测模型，并确定潜在的改进措施以加强临床决策。
癌症患者发生静脉血栓栓塞（VTE）的风险升高，这对死亡率和生活质量有显著影响。传统的风险评估方法未能考虑与癌症相关的促凝变化，使得个体化风险预测成为一项挑战。美国临床肿瘤学会（ASCO）的现行临床指南建议在化疗前进行风险评估，并认可血栓预防作为标准预防措施。由于任何癌症人群在VTE风险方面高度异质，预测CAT风险是一项肿瘤学挑战。为解决这一问题，已开发出不同的预测模型来按风险对患者进行分层，从而实现有针对性的血栓预防。然而，这些模型在准确性和实用性方面存在差异。本综述讨论了这些不同模型的优缺点。
该综述审视了现有的CAT风险预测模型，突出了它们的优势、局限性和诊断性能。它还确定了其他变量，这些变量可以改进这些模型，以提高其在指导临床医生对癌症患者进行更好的风险分层和治疗决策方面的有效性。
